The Role of decision-analytic modelling in German health technology assessments

BACKGROUND: Decision-analytic modelling (DAM) has become a widespread method in health technology assessments (HTA), but the extent to which modelling is used differs among international HTA institutions. In Germany, the use of DAM is optional within HTAs of the German Institute of Medical Documentation and Information (DIMDI). Our study examines the use of DAM in DIMDI HTA reports and its effect on the quality of information provided for health policies. METHODS: A review of all DIMDI HTA reports (from 1998 to September 2012) incorporating an economic assessment was performed. All included reports were divided into two groups: HTAs with DAM and HTAs without DAM. In both groups, reports were categorized according to the quality of information provided for healthcare decision making. RESULTS: Of the sample of 107 DIMDI HTA reports, 17 (15.9%) used DAM for economic assessment. In the group without DAM, conclusions were limited by the quality of economic information in 51.1% of the reports, whereas we did not find limited conclusions in the group with DAM. Furthermore, 24 reports without DAM (26.7%) stated that using DAM would likely improve the quality of information of the economic assessment. CONCLUSION: The use of DAM techniques can improve the quality of HTAs in Germany. When, after a systematic review of existing literature within a HTA, it is clear that DAM is likely to positively affect the quality of the economic assessment DAM should be used.

Modeling height-diameter relationship in pinus pinaster ait. in the forest intervention zone of Lomba, NE-Portugal

In this work, the relationship between diameter at breast height (d) and total height (h) of individual-tree was modeled with the aim to establish provisory height-diameter (h-d) equations for maritime pine (Pinus pinaster Ait.) stands in the Lomba ZIF, Northeast Portugal. Using data collected locally, several local and generalized h-d equations from the literature were tested and adaptations were also considered. Model fitting was conducted by using usual nonlinear least squares (nls) methods. The best local and generalized models selected, were also tested as mixed models applying a first-order conditional expectation (FOCE) approximation procedure and maximum likelihood methods to estimate fixed and random effects. For the calibration of the mixed models and in order to be consistent with the fitting procedure, the FOCE method was also used to test different sampling designs. The results showed that the local h-d equations with two parameters performed better than the analogous models with three parameters. However a unique set of parameter values for the local model can not be used to all maritime pine stands in Lomba ZIF and thus, a generalized model including covariates from the stand, in addition to d, was necessary to obtain an adequate predictive performance. No evident superiority of the generalized mixed model in comparison to the generalized model with nonlinear least squares parameters estimates was observed. On the other hand, in the case of the local model, the predictive performance greatly improved when random effects were included. The results showed that the mixed model based in the local h-d equation selected is a viable alternative for estimating h if variables from the stand are not available. Moreover, it was observed that it is possible to obtain an adequate calibrated response using only 2 to 5 additional h-d measurements in quantile (or random) trees from the distribution of d in the plot (stand). Balancing sampling effort, accuracy and straightforwardness in practical applications, the generalized model from nls fit is recommended. Examples of applications of the selected generalized equation to the forest management are presented, namely how to use it to complete missing information from forest inventory and also showing how such an equation can be incorporated in a stand-level decision support system that aims to optimize the forest management for the maximization of wood volume production in Lomba ZIF maritime pine stands.

Comparison of the traditional pharmaceutical validation method versus an assisted pharmaceutical validation in hospitalized patients

Objective: To analyze pharmaceutical interventions that have been carried out with the support of an automated system for validation of treatments vs. the traditional method without computer support. Method: The automated program, ALTOMEDICAMENTOS® version 0, has 925 052 data with information regarding approximately 20 000 medicines, analyzing doses, administration routes, number of days with such a treatment, dosing in renal and liver failure, interactions control, similar drugs, and enteral medicines. During eight days, in four different hospitals (high complexity with over 1 000 beds, 400-bed intermediate, geriatric and monographic), the same patients and treatments were analyzed using both systems. Results: 3,490 patients were analyzed, with 42 155 different treatments. 238 interventions were performed using the traditional system (interventions 0.56% / possible interventions) vs. 580 (1.38%) with the automated one. Very significant pharmaceutical interventions were 0.14% vs. 0.46%; significant was 0.38% vs. 0.90%; non-significant was 0.05% vs. 0.01%, respectively. If both systems are simultaneously used, interventions are performed in 1.85% vs. 0.56% with just the traditional system. Using only the traditional model, 30.5% of the possible interventions are detected, whereas without manual review and only the automated one, 84% of the possible interventions are detected. Conclusions: The automated system increases pharmaceutical interventions between 2.43 to 3.64 times. According to the results of this study the traditional validation system needs to be revised relying on automated systems. The automated program works correctly in different hospitals.

Awake examination versus DISE for surgical decision making in patients with OSA: A systematic review

OBJECTIVE: Traditionally, upper airway examination is performed while the patient is awake. However, in the past two decades, drug-induced sleep endoscopy (DISE) has been used as a method of tridimensional evaluation of the upper airway during pharmacologically induced sleep. This study aimed to systematically review the evidence regarding the usefulness of DISE compared with that of traditional awake examination for surgical decision making in patients with obstructive sleep apnea (OSA). DATA SOURCES: Scopus, PubMed, and Cochrane Library databases were searched. REVIEW METHODS: Only studies with a primary objective of evaluating the usefulness of DISE for surgical decision making in patients with OSA were selected. The included studies directly compared awake examination data with DISE outcome data in terms of possible influences on surgical decision making and operation success. RESULTS: A total of eight studies with 535 patients were included in this review. Overall, the surgical treatment changed after DISE in 50.24% (standard deviation 8.4) cases. These changes were more frequently associated with structures contributing to hypopharyngeal or laryngeal obstruction. However, these differences do not automatically indicate a higher success rate. CONCLUSION: This review emphasized the direct impact of DISE compared with that of awake examination on surgical decision making in OSA patients. However, it is also clear that the available published studies lack evidence on the association between this impact and surgical outcomes

The Power of the Synthetic Control Method

The synthetic control method (SCM) is a new, popular method developed for the purpose of estimating the effect of an intervention when only one single unit has been exposed. Other similar, unexposed units are combined into a synthetic control unit intended to mimic the evolution in the exposed unit, had it not been subject to exposure. As the inference relies on only a single observational unit, the statistical inferential issue is a challenge. In this paper, we examine the statistical properties of the estimator, study a number of features potentially yielding uncertainty in the estimator, discuss the rationale for statistical inference in relation to SCM, and provide a Web-app for researchers to aid in their decision of whether SCM is powerful for a specific case study. We conclude that SCM is powerful with a limited number of controls in the donor pool and a fairly short pre-intervention time period. This holds as long as the parameter of interest is a parametric specification of the intervention effect, and the duration of post-intervention period is reasonably long, and the fit of the synthetic control unit to the exposed unit in the pre-intervention period is good.

Changes in the spatial distribution of tree species in a terra firme rain forest in Brazilian Amazonia after logging.

The objective of this paper was to determine changes in the spatial distribution of tree species in a logged compared to an unlogged forest of the Tapajos National Forest in the municipality of Belterra, State of Para, Brazil, over an eight-year period. The distribution pattern was determined for trees> 5 cm dbh and, also, for trees > 30 cm dbh. The relationship (a quadrate method) discussed by McGinnis was selected to be used in this study. Forty-seven percent of species with trees > 5 cm dbh showed clumped distribution in the studied forests. Geissospermwn sericeunz Benth & Hook., Minquartia guianensis Aubl., Poureria bilocularis (H. Winkler) Bachni, Protium guacayantan Cuatrec, Sclerolobium chrysophyllunz Poepp. et Endl. and the Sapotaceae family (9 species) occurred in clumps of small trees (5 cm 5 dbh < 30 cm) and big trees (dbh > 30 cm) in both the logged and undisturbed forest. Trees in all sizes of these species certainly have aggregation characteristics in different light condition's during the whole growth-cycle. Only Sclerolobium cizzysophylltan out of fourteen species that occurred aggregated in all forest conditions was light demanding. The shade-tolerant Lecythis lurida (Miers) Mori and Manilkara huberi (Ducke) Stand!. showed also aggregated distribution for small and big trees in the unlogged forest. An aggregated distribution is not always directly correlated to abundance, considering that most of the clumped species had less than seven trees per hectare.

Advanced decision-support methods for the design, control, and optimization of perishable products life cycle

In the last decades, global food supply chains had to deal with the increasing awareness of the stakeholders and consumers about safety, quality, and sustainability. In order to address these new challenges for food supply chain systems, an integrated approach to design, control, and optimize product life cycle is required. Therefore, it is essential to introduce new models, methods, and decision-support platforms tailored to perishable products. This thesis aims to provide novel practice-ready decision-support models and methods to optimize the logistics of food items with an integrated and interdisciplinary approach. It proposes a comprehensive review of the main peculiarities of perishable products and the environmental stresses accelerating their quality decay. Then, it focuses on top-down strategies to optimize the supply chain system from the strategical to the operational decision level. Based on the criticality of the environmental conditions, the dissertation evaluates the main long-term logistics investment strategies to preserve products quality. Several models and methods are proposed to optimize the logistics decisions to enhance the sustainability of the supply chain system while guaranteeing adequate food preservation. The models and methods proposed in this dissertation promote a climate-driven approach integrating climate conditions and their consequences on the quality decay of products in innovative models supporting the logistics decisions. Given the uncertain nature of the environmental stresses affecting the product life cycle, an original stochastic model and solving method are proposed to support practitioners in controlling and optimizing the supply chain systems when facing uncertain scenarios. The application of the proposed decision-support methods to real case studies proved their effectiveness in increasing the sustainability of the perishable product life cycle. The dissertation also presents an industry application of a global food supply chain system, further demonstrating how the proposed models and tools can be integrated to provide significant savings and sustainability improvements.

New perspectives on A.I. in sentencing. Human decision-making between risk assessment tools and protection of humans rights.

The aim of this thesis is to investigate a field that until a few years ago was foreign to and distant from the penal system. The purpose of this undertaking is to account for the role that technology could plays in the Italian Criminal Law system. More specifically, this thesis attempts to scrutinize a very intricate phase of adjudication. After deciding on the type of an individual's liability, a judge must decide on the severity of the penalty. This type of decision implies a prognostic assessment that looks to the future. It is precisely in this field and in prognostic assessments that, as has already been anticipated in the United, instruments and processes are inserted in the pre-trial but also in the decision-making phase. In this contribution, we attempt to describe the current state of this field, trying, as a matter of method, to select the most relevant or most used tools. Using comparative and qualitative methods, the uses of some of these instruments in the supranational legal system are analyzed. Focusing attention on the Italian system, an attempt was made to investigate the nature of the element of an individual's ‘social dangerousness’ (pericolosità sociale) and capacity to commit offences, types of assessments that are fundamental in our system because they are part of various types of decisions, including the choice of the best sanctioning treatment. It was decided to turn our attention to this latter field because it is believed that the judge does not always have the time, the means and the ability to assess all the elements of a subject and identify the best 'individualizing' treatment in order to fully realize the function of Article 27, paragraph 3 of the Constitution.

Smart sampling approaches for Decision-Focused Learning

Many real-word decision- making problems are defined based on forecast parameters: for example, one may plan an urban route by relying on traffic predictions. In these cases, the conventional approach consists in training a predictor and then solving an optimization problem. This may be problematic since mistakes made by the predictor may trick the optimizer into taking dramatically wrong decisions. Recently, the field of Decision-Focused Learning overcomes this limitation by merging the two stages at training time, so that predictions are rewarded and penalized based on their outcome in the optimization problem. There are however still significant challenges toward a widespread adoption of the method, mostly related to the limitation in terms of generality and scalability. One possible solution for dealing with the second problem is introducing a caching-based approach, to speed up the training process. This project aims to investigate these techniques, in order to reduce even more, the solver calls. For each considered method, we designed a particular smart sampling approach, based on their characteristics. In the case of the SPO method, we ended up discovering that it is only necessary to initialize the cache with only several solutions; those needed to filter the elements that we still need to properly learn. For the Blackbox method, we designed a smart sampling approach, based on inferred solutions.

Tree regularized neural networks for key point analysis

This thesis contributes to the ArgMining 2021 shared task on Key Point Analysis. Key Point Analysis entails extracting and calculating the prevalence of a concise list of the most prominent talking points, from an input corpus. These talking points are usually referred to as key points. Key point analysis is divided into two subtasks: Key Point Matching, which involves assigning a matching score to each key point/argument pair, and Key Point Generation, which consists of the generation of key points. The task of Key Point Matching was approached using different models: a pretrained Sentence Transformers model and a tree-constrained Graph Neural Network were tested. The best model was the fine-tuned Sentence Transformers, which achieved a mean Average Precision score of 0.75, ranking 12 compared to other participating teams. The model was then used for the subtask of Key Point Generation using the extractive method in the selection of key point candidates and the model developed for the previous subtask to evaluate them.

A Highly Improved Method For Sensitive Determination Of Amitriptyline In Pharmaceutical Formulations Using An Unmodified Carbon Nanotube Electrode In The Presence Of Sulfuric Acid.

The present paper describes a novel, simple and reliable differential pulse voltammetric method for determining amitriptyline (AMT) in pharmaceutical formulations. It has been described for many authors that this antidepressant is electrochemically inactive at carbon electrodes. However, the procedure proposed herein consisted in electrochemically oxidizing AMT at an unmodified carbon nanotube paste electrode in the presence of 0.1 mol L(-1) sulfuric acid used as electrolyte. At such concentration, the acid facilitated the AMT electroxidation through one-electron transfer at 1.33 V vs. Ag/AgCl, as observed by the augmentation of peak current. Concerning optimized conditions (modulation time 5 ms, scan rate 90 mV s(-1), and pulse amplitude 120 mV) a linear calibration curve was constructed in the range of 0.0-30.0 μmol L(-1), with a correlation coefficient of 0.9991 and a limit of detection of 1.61 μmol L(-1). The procedure was successfully validated for intra- and inter-day precision and accuracy. Moreover, its feasibility was assessed through analysis of commercial pharmaceutical formulations and it has been compared to the UV-vis spectrophotometric method used as standard analytical technique recommended by the Brazilian Pharmacopoeia.

Reasons For Brazilian Women To Switch From Different Contraceptives To Long-acting Reversible Contraceptives.

Long-acting reversible contraceptives (LARCs) include the copper-releasing intrauterine device (IUD), the levonorgestrel-releasing intrauterine system (LNG-IUS) and implants. Despite the high contraceptive efficacy of LARCs, their prevalence of use remains low in many countries. The objective of this study was to assess the main reasons for switching from contraceptive methods requiring daily or monthly compliance to LARC methods within a Brazilian cohort. Women of 18-50 years of age using different contraceptives and wishing to switch to a LARC method answered a questionnaire regarding their motivations for switching from their current contraceptive. Continuation rates were evaluated 1 year after method initiation. Sample size was calculated at 1040 women. Clinical performance was evaluated by life table analysis. The cutoff date for analysis was May 23, 2013. Overall, 1167 women were interviewed; however, after 1 year of use, the medical records of only 1154 women were available for review. The main personal reason for switching, as reported by the women, was fear of becoming pregnant while the main medical reasons were nausea and vomiting and unscheduled bleeding. No pregnancies occurred during LARC use, and the main reasons for discontinuation were expulsion (in the case of the IUD and LNG-IUS) and a decision to undergo surgical sterilization (in the case of the etonogestrel-releasing implant). Continuation rate was ~95.0/100 women/year for the three methods. Most women chose a LARC method for its safety and for practical reasons, and after 1 year of use, most women continued with the method.

De Novo Assembly And Transcriptome Analysis Of The Rubber Tree (hevea Brasiliensis) And Snp Markers Development For Rubber Biosynthesis Pathways.

Hevea brasiliensis (Willd. Ex Adr. Juss.) Muell.-Arg. is the primary source of natural rubber that is native to the Amazon rainforest. The singular properties of natural rubber make it superior to and competitive with synthetic rubber for use in several applications. Here, we performed RNA sequencing (RNA-seq) of H. brasiliensis bark on the Illumina GAIIx platform, which generated 179,326,804 raw reads on the Illumina GAIIx platform. A total of 50,384 contigs that were over 400 bp in size were obtained and subjected to further analyses. A similarity search against the non-redundant (nr) protein database returned 32,018 (63%) positive BLASTx hits. The transcriptome analysis was annotated using the clusters of orthologous groups (COG), gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Pfam databases. A search for putative molecular marker was performed to identify simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). In total, 17,927 SSRs and 404,114 SNPs were detected. Finally, we selected sequences that were identified as belonging to the mevalonate (MVA) and 2-C-methyl-D-erythritol 4-phosphate (MEP) pathways, which are involved in rubber biosynthesis, to validate the SNP markers. A total of 78 SNPs were validated in 36 genotypes of H. brasiliensis. This new dataset represents a powerful information source for rubber tree bark genes and will be an important tool for the development of microsatellites and SNP markers for use in future genetic analyses such as genetic linkage mapping, quantitative trait loci identification, investigations of linkage disequilibrium and marker-assisted selection.

Influence Of Delivery Method On Neuroprotection By Bone Marrow Mononuclear Cell Therapy Following Ventral Root Reimplantation With Fibrin Sealant.

The present work compared the local injection of mononuclear cells to the spinal cord lateral funiculus with the alternative approach of local delivery with fibrin sealant after ventral root avulsion (VRA) and reimplantation. For that, female adult Lewis rats were divided into the following groups: avulsion only, reimplantation with fibrin sealant; root repair with fibrin sealant associated with mononuclear cells; and repair with fibrin sealant and injected mononuclear cells. Cell therapy resulted in greater survival of spinal motoneurons up to four weeks post-surgery, especially when mononuclear cells were added to the fibrin glue. Injection of mononuclear cells to the lateral funiculus yield similar results to the reimplantation alone. Additionally, mononuclear cells added to the fibrin glue increased neurotrophic factor gene transcript levels in the spinal cord ventral horn. Regarding the motor recovery, evaluated by the functional peroneal index, as well as the paw print pressure, cell treated rats performed equally well as compared to reimplanted only animals, and significantly better than the avulsion only subjects. The results herein demonstrate that mononuclear cells therapy is neuroprotective by increasing levels of brain derived neurotrophic factor (BDNF) and glial derived neurotrophic factor (GDNF). Moreover, the use of fibrin sealant mononuclear cells delivery approach gave the best and more long lasting results.

Leaf-, Panel- And Latex-expressed Sequenced Tags From The Rubber Tree (hevea Brasiliensis) Under Cold-stressed And Suboptimal Growing Conditions: The Development Of Gene-targeted Functional Markers For Stress Response.

Hevea brasiliensis is a native species of the Amazon Basin of South America and the primary source of natural rubber worldwide. Due to the occurrence of South American Leaf Blight disease in this area, rubber plantations have been extended to suboptimal regions. Rubber tree breeding is time-consuming and expensive, but molecular markers can serve as a tool for early evaluation, thus reducing time and costs. In this work, we constructed six different cDNA libraries with the aim of developing gene-targeted molecular markers for the rubber tree. A total of 8,263 reads were assembled, generating 5,025 unigenes that were analyzed; 912 expressed sequence tags (ESTs) represented new transcripts, and two sequences were highly up-regulated by cold stress. These unigenes were scanned for microsatellite (SSR) regions and single nucleotide polymorphisms (SNPs). In total, 169 novel EST-SSR markers were developed; 138 loci were polymorphic in the rubber tree, and 98 % presented transferability to six other Hevea species. Locus duplication was observed in H. brasiliensis and other species. Additionally, 43 SNP markers in 13 sequences that showed similarity to proteins involved in stress response, latex biosynthesis and developmental processes were characterized. cDNA libraries are a rich source of SSR and SNP markers and enable the identification of new transcripts. The new markers developed here will be a valuable resource for linkage mapping, QTL identification and other studies in the rubber tree and can also be used to evaluate the genetic variability of other Hevea species, which are valuable assets in rubber tree breeding.