The incremental healthcare cost associated with cancer in Belgium: A registry-based data analysis.

BACKGROUND: Similar to many countries, Belgium experienced a rapid increase in cancer diagnoses in the last years. Considering that a large part of cancer types could be prevented, our study aimed to estimate the annual healthcare burden of cancer per site, and to compare cost with burden of disease estimates to have a better understanding of the impact of different cancer sites in Belgium. METHODS: We used nationally available data sources to estimate the healthcare expenditure. We opted for a prevalence-based approach which measures the disease attributable costs that occur concurrently for 10-year prevalent cancer cases in 2018. Average attributable costs of cancer were computed via matching of cases (patients with cancer by site) and controls (patients without cancer). Years of life lost due to disability (YLD) were used to summarize the health impact of the selected cancers. RESULTS: The highest attributable cost in 2018 among the selected cancers was on average €15,867 per patient for bronchus and lung cancer, followed by liver cancer, pancreatic cancer, and mesothelioma. For the total cost, lung cancer was the most costly cancer site with almost €700 million spent in 2018. Lung cancer was followed by breast and colorectal cancer that costed more than €300 million each in 2018. CONCLUSIONS: In our study, the direct attributable cost of the most prevalent cancer sites in Belgium was estimated to provide useful guidance for cost containment policies. Many of these cancers could be prevented by tackling risk factors such as smoking, obesity, and environmental stressors.

The health and economic burden of musculoskeletal disorders in Belgium from 2013 to 2018.

INTRODUCTION: Low back pain (LBP), neck pain (NKP), osteoarthritis (OST) and rheumatoid arthritis (RHE) are among the musculoskeletal (MSK) disorders causing the greatest disability in terms of Years Lived with Disability. The current study aims to analyze the health and economic impact of these MSK disorders in Belgium, providing a summary of morbidity and mortality outcomes from 2013 to 2018, as well as direct and indirect costs from 2013 to 2017. METHODS: The health burden of LBP, NKP, OST and RHE in Belgium from 2013 to 2018 was summarized in terms of prevalence and disability-adjusted life years (DALY) using data from the Belgian health interview surveys (BHIS), the INTEGO database (Belgian registration network for general practitioners) and the Global Burden of Diseases study 2019. The economic burden included estimates of direct medical costs and indirect costs, measured by cost of work absenteeism. For this purpose, data of the respondents to the BHIS-2013 were linked with the national health insurance data (intermutualistic agency [IMA] database) 2013-2017. RESULTS: In 2018, 2.5 million Belgians were affected by at least one MSK disorder. OST represented the disorder with the highest number of cases for both men and women, followed by LBP. In the same year, MSK disorders contributed to a total of 180,746 DALYs for female and 116,063 DALYs for men. LBP appeared to be the largest contributor to the health burden of MSK. Having at least one MSK disorder costed on average 3 billion € in medical expenses and 2 billion € in indirect costs per year, with LBP being the most costly. CONCLUSION: MSK disorders represent a major health and economic burden in Belgium. As their burden will probably continue to increase in the future, acting on the risk factors associated to these disorders is crucial to mitigate both the health and economic burden.

Health care costs and lost productivity costs related to excess weight in Belgium.

BACKGROUND: This study aimed to estimate annual health care and lost productivity costs associated with excess weight among the adult population in Belgium, using national health data. METHODS: Health care costs and costs of absenteeism were estimated using data from the Belgian national health interview survey (BHIS) 2013 linked with individual health insurance data (2013-2017). Average yearly health care costs and costs of absenteeism were assessed by body mass index (BMI) categories - i.e., underweight (BMI < 18.5 kg/m2), normal weight (18.5 ≤ BMI < 25 kg/m2), overweight (25 ≤ BMI < 30 kg/m2) and obesity (BMI ≥ 30 kg/m2). Health care costs were also analysed by type of cost (i.e. ambulatory, hospital, reimbursed medication). The cost attributable to excess weight and the contribution of various other chronic conditions to the incremental cost of excess weight were estimated using the method of recycled prediction (a.k.a. standardisation). RESULTS: According to BHIS 2013, 34.7% and 13.9% of the Belgian adult population were respectively affected by overweight or obesity. They were mostly concentrated in the age-group 35-65 years and had significantly more chronic conditions compared to the normal weight population. Average total healthcare expenses for people with overweight and obesity were significantly higher than those observed in the normal weight population. The adjusted incremental annual health care cost of excess weight in Belgium was estimated at €3,329,206,657 (€651 [95% CI: €144-€1,084] and €1,015 [95% CI: €343-€1,697] per capita for individuals with overweight and obesity respectively). The comorbidities identified to be the main drivers for these incremental health care costs were hypertension, high cholesterol, serious gloom and depression. Mean annual incremental cost of absenteeism for overweight accounted for €242 per capita but was not statistically significant, people with obesity showed a significantly higher cost (p < 0.001) compared to the normal weight population: €2,015 [95% CI: €179-€4,336] per capita. The annual total incremental costs due to absenteeism of the population affected by overweight and obesity was estimated at €1,209,552,137. Arthritis, including rheumatoid arthritis and osteoarthritis, was the most important driver of the incremental cost of absenteeism in individuals with overweight and obesity, followed by hypertension and low back pain. CONCLUSIONS: The mean annual incremental cost of excess weight in Belgium is of concern and stresses the need for policy actions aiming to reduce excess body weight. This study can be used as a baseline to evaluate the potential savings and health benefits of obesity prevention interventions.

Nonlinear mediation analysis with high-dimensional mediators whose causal structure is unknown.

With multiple possible mediators on the causal pathway from a treatment to an outcome, we consider the problem of decomposing the effects along multiple possible causal path(s) through each distinct mediator. Under a path-specific effects framework, such fine-grained decompositions necessitate stringent assumptions, such as correctly specifying the causal structure among the mediators, and no unobserved confounding among the mediators. In contrast, interventional direct and indirect effects for multiple mediators can be identified under much weaker conditions, while providing scientifically relevant causal interpretations. Nonetheless, current estimation approaches require (correctly) specifying a model for the joint mediator distribution, which can be difficult when there is a high-dimensional set of possibly continuous and noncontinuous mediators. In this article, we avoid the need to model this distribution, by developing a definition of interventional effects previously suggested for longitudinal mediation. We propose a novel estimation strategy that uses nonparametric estimates of the (counterfactual) mediator distributions. Noncontinuous outcomes can be accommodated using nonlinear outcome models. Estimation proceeds via Monte Carlo integration. The procedure is illustrated using publicly available genomic data to assess the causal effect of a microRNA expression on the 3-month mortality of brain cancer patients that is potentially mediated by expression values of multiple genes.

Quality assessment practice in systematic reviews of mediation studies: results from an overview of systematic reviews.

OBJECTIVE: To describe the bias assessment practice in recently published systematic reviews of mediation studies and to evaluate the quality of different bias assessment tools for mediation analysis proposed in the literature. METHOD: We conducted an overview of systematic reviews by searching MEDLINE (OvidSP), PsycINFO (OvidSP), Cochrane Database of Systematic Reviews (OvidSP), and PubMed databases for systematic reviews of mediation studies published from 2007 to 2020. Two reviewers independently screened the title, abstracts, and full texts of the identified reports and extracted the data. The publications of all mediation-specific quality assessment tools used in these reviews were also identified for the evaluation of the tools' development and validation. RESULT: Among 103 eligible reviews, 24 (23%) reviews did not assess the risk of bias of eligible studies, and 48 (47%) assessed risk of bias using a tool that was not specifically designed to evaluate mediation analysis. 31 (30.1%) reviews assessed the risk of mediation-specific biases, either narratively or by using specific tools for mediation studies. However, none of these tools were consensus-based, rigorously developed or validated. CONCLUSION: The quality assessment practice in recently published systematic reviews of mediation studies is suboptimal. To improve the quality and consistency of risk of bias assessments for mediation studies, a consensus-based bias assessment tool is needed.

Efficient, doubly robust estimation of the effect of dose switching for switchers in a randomized clinical trial.

Motivated by a clinical trial conducted by Janssen Pharmaceutica in which a flexible dosing regimen is compared to placebo, we evaluate how switchers in the treatment arm (i.e., patients who were switched to the higher dose) would have fared had they been kept on the low dose. This is done in order to understand whether flexible dosing is potentially beneficial for them. Simply comparing these patients' responses with those of patients who stayed on the low dose does not likely entail a satisfactory evaluation because the latter patients are usually in a better health condition. Because the available information in the considered trial is too limited to enable a reliable adjustment, we will instead transport data from a fixed dosing trial that has been conducted concurrently on the same target, albeit not in an identical patient population. In particular, we propose an estimator that relies on an outcome model, a model for switching, and a propensity score model for the association between study and patient characteristics. The proposed estimator is asymptotically unbiased if either the outcome or the propensity score model is correctly specified, and efficient (under the semiparametric model where the randomization probabilities are known and independent of baseline covariates) when all models are correctly specified. The proposed method for transporting information from an external study is more broadly applicable in studies where a classical confounding adjustment is not possible due to near positivity violation (e.g., studies where switching takes place in a (near) deterministic manner). Monte Carlo simulations and application to the motivating study demonstrate adequate performance.

Improving interim decisions in randomized trials by exploiting information on short-term endpoints and prognostic baseline covariates.

Conditional power calculations are frequently used to guide the decision whether or not to stop a trial for futility or to modify planned sample size. These ignore the information in short-term endpoints and baseline covariates, and thereby do not make fully efficient use of the information in the data. We therefore propose an interim decision procedure based on the conditional power approach which exploits the information contained in baseline covariates and short-term endpoints. We will realize this by considering the estimation of the treatment effect at the interim analysis as a missing data problem. This problem is addressed by employing specific prediction models for the long-term endpoint which enable the incorporation of baseline covariates and multiple short-term endpoints. We show that the proposed procedure leads to an efficiency gain and a reduced sample size, without compromising the Type I error rate of the procedure, even when the adopted prediction models are misspecified. In particular, implementing our proposal in the conditional power approach enables earlier decisions relative to standard approaches, whilst controlling the probability of an incorrect decision. This time gain results in a lower expected number of recruited patients in case of stopping for futility, such that fewer patients receive the futile regimen. We explain how these methods can be used in adaptive designs with unblinded sample size re-assessment based on the inverse normal P-value combination method to control Type I error. We support the proposal by Monte Carlo simulations based on data from a real clinical trial.

A novel approach for identifying and addressing case-mix heterogeneity in individual participant data meta-analysis.

Case-mix heterogeneity across studies complicates meta-analyses. As a result of this, treatments that are equally effective on patient subgroups may appear to have different effectiveness on patient populations with different case mix. It is therefore important that meta-analyses be explicit for what patient population they describe the treatment effect. To achieve this, we develop a new approach for meta-analysis of randomized clinical trials, which use individual patient data (IPD) from all trials to infer the treatment effect for the patient population in a given trial, based on direct standardization using either outcome regression (OCR) or inverse probability weighting (IPW). Accompanying random-effect meta-analysis models are developed. The new approach enables disentangling heterogeneity due to case mix from that due to beyond case-mix reasons.

Ethical decision-making climate in the ICU: theoretical framework and validation of a self-assessment tool.

BACKGROUND: Literature depicts differences in ethical decision-making (EDM) between countries and intensive care units (ICU). OBJECTIVES: To better conceptualise EDM climate in the ICU and to validate a tool to assess EDM climates. METHODS: Using a modified Delphi method, we built a theoretical framework and a self-assessment instrument consisting of 35 statements. This Ethical Decision-Making Climate Questionnaire (EDMCQ) was developed to capture three EDM domains in healthcare: interdisciplinary collaboration and communication; leadership by physicians; and ethical environment. This instrument was subsequently validated among clinicians working in 68 adult ICUs in 13 European countries and the USA. Exploratory and confirmatory factor analysis was used to determine the structure of the EDM climate as perceived by clinicians. Measurement invariance was tested to make sure that variables used in the analysis were comparable constructs across different groups. RESULTS: Of 3610 nurses and 1137 physicians providing ICU bedside care, 2275 (63.1%) and 717 (62.9%) participated respectively. Statistical analyses revealed that a shortened 32-item version of the EDMCQ scale provides a factorial valid measurement of seven facets of the extent to which clinicians perceive an EDM climate: self-reflective and empowering leadership by physicians; practice and culture of open interdisciplinary reflection; culture of not avoiding end-of-life decisions; culture of mutual respect within the interdisciplinary team; active involvement of nurses in end-of-life care and decision-making; active decision-making by physicians; and practice and culture of ethical awareness. Measurement invariance of the EDMCQ across occupational groups was shown, reflecting that nurses and physicians interpret the EDMCQ items in a similar manner. CONCLUSIONS: The 32-item version of the EDMCQ might enrich the EDM climate measurement, clinicians' behaviour and the performance of healthcare organisations. This instrument offers opportunities to develop tailored ICU team interventions.

Gonadotropin Therapy versus Laparoscopic Ovarian Drilling in Clomiphene Citrate-Resistant Polycystic Ovary Syndrome Patients: A Retrospective Cost-Effectiveness Analysis.

BACKGROUND: Gonadotropin therapy and laparoscopic ovarian drilling (LOD) are treatment options for ovulation induction (OI) in clomiphene citrate (CC)-resistant polycystic ovary syndrome (PCOS) patients. The current evidence of the cost-effectiveness of both treatments is scarce, conflicting and performed from different health-economic perspectives. METHODS: A retrospective health-economic evaluation was performed from a societal perspective in which human menopausal gonadotropin (hMG) therapy (n = 43) was compared with LOD (n = 35), followed by OI with CC and/or hMG if spontaneous ovulation did not occur within 2 months. Data were collected until the patients were pregnant, with a time limit of 6 months after the onset of treatment. Outcomes were expressed as ongoing pregnancy rate and number of live-born children. RESULTS: The ongoing pregnancy rate was 21/35 (60%) after LOD and 30/43 (69.8%) after hMG treatment (relative risk 0.85, 95% CI 0.61-1.19). The societal cost per patient, up to an ongoing pregnancy, was significantly higher after LOD versus hMG treatment (adjusted mean difference EUR 1,073, 95% CI 180-1,967). CONCLUSION: This economic evaluation based on real-life data shows that the societal cost up to an ongoing pregnancy is less after hMG treatment when compared with LOD surgery in CC-resistant PCOS patients.

Preventable proportion of severe infections acquired in intensive care units: case-mix adjusted estimations from patient-based surveillance data.

BACKGROUND: More than 10% of patients admitted to intensive care units (ICUs) experience a severe, healthcare-associated infection, such as ventilator-associated pneumonia (VAP) or bloodstream infection (BSI). What could be a public health target for prevention is hotly debated, because properly adjusting for intrinsic risk factors in the patient population is difficult. We aimed to estimate the proportion of ICU-acquired VAP and BSI cases that are amenable to prevention in routine conditions. METHODS: We analyzed routine data collected prospectively according to the European standard protocol for patient-based surveillance of healthcare-acquired infections in ICUs. We computed the number of infections to be expected if, after adjustment for case mix, the infection incidence in ICUs with higher infection rates could be reduced to that of the top-tenth-percentile-ranked ICU. Computations came from model-based simulation of individual patient profiles over time in the ICU. The preventable proportion was computed as the number of observed cases minus the number of expected cases divided by the number of observed cases. RESULTS: Data for 78,222 patients admitted for more than 2 days to 525 ICUs in 6 European countries from 2005 to 2008 were available for analysis. We calculated that 52% of VAP and 69% of BSI was preventable. CONCLUSIONS: Our pragmatic, if highly conservative, estimates quantify the potential for prevention of VAP and BSI in routine conditions, assuming that variation in infection incidence between ICUs can be eliminated with improved quality of care, apart from variation attributable to differential case mix.

Direct genetic effects and their estimation from matched case-control data.

In genetic association studies, a single marker is often associated with multiple, correlated phenotypes (e.g., obesity and cardiovascular disease, or nicotine dependence and lung cancer). A pervasive question is then whether that marker exerts independent effects on all phenotypes. In this paper, we address this question by assessing whether there is a genetic effect on one phenotype that is not mediated through the other ones, so called direct genetic effect. Answering such question may represent an important step in the elucidation of the underlying biological mechanism. Under rather restrictive conditions, such direct genetic effects are known to be estimable by standard regression methods. Under more lenient conditions, in a prospective or unmatched case-control study, these effects can be estimated by using a previously proposed G-estimation method (Vansteelandt [2009] Epidemiology 20, 851-860). The present paper extends this method to matched case-control studies, and investigates the conditions under which this extension is valid. We illustrate the method on data from a matched case-control study, which we use to elucidate the pathway implications of a detected association between myocardial infarction and a genetic locus in the chromosomal region of the FTO gene.

Parental socioeconomic status and soft drink consumption of the child. The mediating proportion of parenting practices.

The hypothesis of this study is twofold and states that parental socioeconomic status has an effect on the soft drink consumption of the child, and that this effect is mediated by the soft drink related parenting practices. One thousand six hundred and thirty-nine parents of 2.5-7 year old children from 34 Flemish pre-primary and primary schools, completed a self-administered questionnaire on sociodemographic characteristics, soft drink consumption and soft drink related parenting practices. Causal mediation analyses showed an effect of socioeconomic status on soft drink consumption of the child: children from high socioeconomic status consume 0.42 times the amount of soft drinks of children from lower socioeconomic status. Interestingly, this effect is almost entirely mediated by three soft drink parenting practices: soft drinks served at meals, the child can take soft drink whenever he or she wants and having soft drinks at home.

Context-dependent codon partition models provide significant increases in model fit in atpB and rbcL protein-coding genes.

BACKGROUND: Accurate modelling of substitution processes in protein-coding sequences is often hampered by the computational burdens associated with full codon models. Lately, codon partition models have been proposed as a viable alternative, mimicking the substitution behaviour of codon models at a low computational cost. Such codon partition models however impose independent evolution of the different codon positions, which is overly restrictive from a biological point of view. Given that empirical research has provided indications of context-dependent substitution patterns at four-fold degenerate sites, we take those indications into account in this paper. RESULTS: We present so-called context-dependent codon partition models to assess previous empirical claims that the evolution of four-fold degenerate sites is strongly dependent on the composition of its two flanking bases. To this end, we have estimated and compared various existing independent models, codon models, codon partition models and context-dependent codon partition models for the atpB and rbcL genes of the chloroplast genome, which are frequently used in plant systematics. Such context-dependent codon partition models employ a full dependency scheme for four-fold degenerate sites, whilst maintaining the independence assumption for the first and second codon positions. CONCLUSIONS: We show that, both in the atpB and rbcL alignments of a collection of land plants, these context-dependent codon partition models significantly improve model fit over existing codon partition models. Using Bayes factors based on thermodynamic integration, we show that in both datasets the same context-dependent codon partition model yields the largest increase in model fit compared to an independent evolutionary model. Context-dependent codon partition models hence perform closer to codon models, which remain the best performing models at a drastically increased computational cost, compared to codon partition models, but remain computationally interesting alternatives to codon models. Finally, we observe that the substitution patterns in both datasets are drastically different, leading to the conclusion that combined analysis of these two genes using a single model may not be advisable from a context-dependent point of view.

Modelling the ancestral sequence distribution and model frequencies in context-dependent models for primate non-coding sequences.

BACKGROUND: Recent approaches for context-dependent evolutionary modelling assume that the evolution of a given site depends upon its ancestor and that ancestor's immediate flanking sites. Because such dependency pattern cannot be imposed on the root sequence, we consider the use of different orders of Markov chains to model dependence at the ancestral root sequence. Root distributions which are coupled to the context-dependent model across the underlying phylogenetic tree are deemed more realistic than decoupled Markov chains models, as the evolutionary process is responsible for shaping the composition of the ancestral root sequence. RESULTS: We find strong support, in terms of Bayes Factors, for using a second-order Markov chain at the ancestral root sequence along with a context-dependent model throughout the remainder of the phylogenetic tree in an ancestral repeats dataset, and for using a first-order Markov chain at the ancestral root sequence in a pseudogene dataset. Relaxing the assumption of a single context-independent set of independent model frequencies as presented in previous work, yields a further drastic increase in model fit. We show that the substitution rates associated with the CpG-methylation-deamination process can be modelled through context-dependent model frequencies and that their accuracy depends on the (order of the) Markov chain imposed at the ancestral root sequence. In addition, we provide evidence that this approach (which assumes that root distribution and evolutionary model are decoupled) outperforms an approach inspired by the work of Arndt et al., where the root distribution is coupled to the evolutionary model. We show that the continuous-time approximation of Hwang and Green has stronger support in terms of Bayes Factors, but the parameter estimates show minimal differences. CONCLUSIONS: We show that the combination of a dependency scheme at the ancestral root sequence and a context-dependent evolutionary model across the remainder of the tree allows for accurate estimation of the model's parameters. The different assumptions tested in this manuscript clearly show that designing accurate context-dependent models is a complex process, with many different assumptions that require validation. Further, these assumptions are shown to change across different datasets, making the search for an adequate model for a given dataset quite challenging.

Psychological well-being and socio-economic hardship among AIDS orphans and other vulnerable children in Guinea.

Over the past decade, the effects of AIDS-related parental death on children's socio-economic, educational and psychological well-being have become apparent. Most studies, however, have compared the plight of so-called AIDS orphans with non-orphaned children only. Consequently, such study designs are unable to establish if the AIDS-related cause of death of the parents confers effects additional to those of parent-bereavement. We therefore conducted a cross-sectional survey to assess the psychological well-being and socio-economic hardship among 140 non-orphaned children, 133 children orphaned by causes other than AIDS (O) and 124 children orphaned by AIDS (O-A) in Conakry, N'Zerekore and the villages around N'Zerekore, Guinea. Multi-way analysis of variance and multiple (ordinal) logistic regression models were applied to measure the association between the orphan status and psychological well-being, school attendance, economic activities, frequency of going to bed hungry and sleeping commodity. After adjustment for confounding factors, the psychological well-being score (PWS) was significantly lower among AIDS-orphaned children than among O (P<0.001). Additionally, AIDS-orphaned children were more likely to be engaged in economic activities (adjusted odds ratio (AOR) = 3.04; 95% CI: 1.45-6.36) and to go to bed hungry on a daily basis (AOR = 2.73; 95% CI: 1.24-6.02) than other orphans. The differences in school attendance and the proportion of children with a bed or couch to sleep between AIDS-orphaned children and O were not statistically significant. This situation calls for sustainable and holistic approaches to ensure the psychological and socio-economic stability of AIDS orphans and other vulnerable children.

A model-based approach to study nearest-neighbor influences reveals complex substitution patterns in non-coding sequences.

In this article, we present a likelihood-based framework for modeling site dependencies. Our approach builds upon standard evolutionary models but incorporates site dependencies across the entire tree by letting the evolutionary parameters in these models depend upon the ancestral states at the neighboring sites. It thus avoids the need for introducing new and high-dimensional evolutionary models for site-dependent evolution. We propose a Markov chain Monte Carlo approach with data augmentation to infer the evolutionary parameters under our model. Although our approach allows for wide-ranging site dependencies, we illustrate its use, in two non-coding datasets, in the case of nearest-neighbor dependencies (i.e., evolution directly depending only upon the immediate flanking sites). The results reveal that the general time-reversible model with nearest-neighbor dependencies substantially improves the fit to the data as compared to the corresponding model with site independence. Using the parameter estimates from our model, we elaborate on the importance of the 5-methylcytosine deamination process (i.e., the CpG effect) and show that this process also depends upon the 5' neighboring base identity. We hint at the possibility of a so-called TpA effect and show that the observed substitution behavior is very complex in the light of dinucleotide estimates. We also discuss the presence of CpG effects in a nuclear small subunit dataset and find significant evidence that evolutionary models incorporating context-dependent effects perform substantially better than independent-site models and in some cases even outperform models that incorporate varying rates across sites.