Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots.

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Newborn screening algorithm distinguishing potential symptomatic isovaleric acidemia from asymptomatic newborns.

Newborn screening (NBS) for isovaleric acidemia (IVA) reduces mortality and morbidity; however, it has also resulted in the detection of individuals with an asymptomatic or mild presentation for which early detection via newborn screening has not been proven to alter neurological outcome. We reevaluated biochemical and molecular data for newborns flagged positive for IVA in aim of developing a new screening algorithm to exclude the latter from positive screening. Among 2 794 365 newborns underwent routine newborn screening in Israel, 412 flagged positive for IVA, of which, 371 were false positives on recall sample testing and 41 positive newborns were referred to the clinic. 38/41 have biochemical and molecular confirmation in keeping with IVA. Among the 38 patients, 32% (12/38) were classified as symptomatic while, 68% (26/38) were classified as asymptomatic. 69% of the latter group harbor the known variant associated with mild potentially asymptomatic phenotype, c.932C>T; p. Ala311Val. Among asymptomatic patients, only 46% (12/26) are currently treated. Two novel variants have been detected in the IVD gene: c.487G>A; p. Ala163Thr and c.985A>G; p. Met329Val. Cut-off recalculation, of referred newborns' initial biochemical results, after classifying the referred patients to two binary groups of symptomatic and asymptomatic, resulted in an improved NBS algorithm comprising of C5 >5 µM and C5/C2>0.2 and C5/C3>4 flagging only those likely to have the classic symptomatic phenotype.

Controlling for false discoveries subsequently to large scale one-way ANOVA testing in proteomics: Practical considerations.

In discovery proteomics, as well as many other "omic" approaches, the possibility to test for the differential abundance of hundreds (or of thousands) of features simultaneously is appealing, despite requiring specific statistical safeguards, among which controlling for the false discovery rate (FDR) has become standard. Moreover, when more than two biological conditions or group treatments are considered, it has become customary to rely on the one-way analysis of variance (ANOVA) framework, where a first global differential abundance landscape provided by an omnibus test can be subsequently refined using various post-hoc tests (PHTs). However, the interactions between the FDR control procedures and the PHTs are complex, because both correspond to different types of multiple test corrections (MTCs). This article surveys various ways to orchestrate them in a data processing workflow and discusses their pros and cons.

Resetting our expectations for parasites and their effects on species interactions: a meta-analysis.

Despite the ubiquitous nature of parasitism, how parasitism alters the outcome of host-species interactions such as competition, mutualism and predation remains unknown. Using a phylogenetically informed meta-analysis of 154 studies, we examined how the mean and variance in the outcomes of species interactions differed between parasitized and non-parasitized hosts. Overall, parasitism did not significantly affect the mean or variance of host-species interaction outcomes, nor did the shared evolutionary histories of hosts and parasites have an effect. Instead, there was considerable variation in outcomes, ranging from strongly detrimental to strongly beneficial for infected hosts. Trophically-transmitted parasites increased the negative effects of predation, parasites increased and decreased the negative effects of interspecific competition for parasitized and non-parasitized heterospecifics, respectively, and parasites had particularly strong negative effects on host species interactions in freshwater and marine habitats, yet were beneficial in terrestrial environments. Our results illuminate the diverse ways in which parasites modify critical linkages in ecological networks, implying that whether the cumulative effects of parasitism are considered detrimental depends not only on the interactions between hosts and their parasites but also on the many other interactions that hosts experience.

Matrix decomposition for modeling lesion development processes in multiple sclerosis.

Our main goal is to study and quantify the evolution of multiple sclerosis lesions observed longitudinally over many years in multi-sequence structural magnetic resonance imaging (sMRI). To achieve that, we propose a class of functional models for capturing the temporal dynamics and spatial distribution of the voxel-specific intensity trajectories in all sMRI sequences. To accommodate the hierarchical data structure (observations nested within voxels, which are nested within lesions, which, in turn, are nested within study participants), we use structured functional principal component analysis. We propose and evaluate the finite sample properties of hypothesis tests of therapeutic intervention effects on lesion evolution while accounting for the multilevel structure of the data. Using this novel testing strategy, we found statistically significant differences in lesion evolution between treatment groups.

Extracorporeal membrane oxygenation for COVID-19-associated acute respiratory distress syndrome: A nationwide analysis.

Extracorporeal membrane oxygenation (ECMO) has been used for COVID-19-associated acute respiratory distress syndrome (ARDS). We aimed to elucidate the association between ECMO and mortality in patients with COVID-19-associated ARDS in the nationwide setting. United States National Inpatient Sample was used to identify mechanically ventilated adults for COVID-19 with ARDS. We divided them into three groups according to the use of ECMO (i.e., no-ECMO, venovenous [VV]-ECMO, and venoarterial [VA]-ECMO). The primary outcome was in-hospital mortality, while the secondary outcomes included the length of hospital stay (LOS) and the total costs during hospitalization. We performed a stepwise logistic regression, adjusting for baseline characteristics, comorbidities, and severity. We included 68 795 (mean age [SD]: 63.5 [0.1]), 3280 (mean age [SD]: 48.7 [0.5]), and 340 (mean age [SD]: 43.3 [2.1]) patients who received no-, VV-, and VA-ECMO, respectively. The logistic regression analysis did not show significant associations between the use of VV-/VA-ECMO and mortality (adjusted odds ratio with no-ECMO as reference [95% confidence interval]: 1.03 [0.86-1.24] and 1.18 [0.64-2.15], respectively). While LOS was longest with VV-ECMO, the total costs were highest with VA-ECMO. In conclusion, our study found no association between the use of ECMO and mortality of COVID-19-associated ARDS in the nationwide setting.

Enhanced recombination among Omicron subvariants of SARS-CoV-2 contributes to viral immune escape.

Genetic recombination is an important driver of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution, which requires the coinfection of a single host cell with different SARS-CoV-2 strains. To understand the emergence and prevalence of recombinant SARS-CoV-2 lineages through time and space, we analyzed SARS-CoV-2 genome sequences collected from November 2019 to July 2022. We observed an extraordinary increase in the emergence of SARS-CoV-2 recombinant lineages during the Omicron wave, particularly in Northern America and Europe. This phenomenon was independent of the sequencing frequency or genetic diversity of circulating SARS-CoV-2 strains. The recombination breakpoints were more prevalent in the 3'-untranslated region of the viral genome. Importantly, we noted the enrichment of certain amino acids in the Spike protein of recombinant lineages, which have been reported to confer immune escape from neutralizing antibodies and increase angiotensin-converting enzyme 2 receptor binding in some cases. We also observed I42V amino acid change genetically fixated in the NSP14 of the Omicron lineage, which needs further characterization for its potential role in enhanced recombination. Overall, we report the important and timely observation of accelerated recombination in the currently circulating SARS-CoV-2 Omicron variants and explore their potential contribution to viral fitness, particularly immune escape.

Clustering of lifestyle behaviours and analysis of their associations with MAFLD: a cross-sectional study of 196,515 individuals in China.

BACKGROUND: The aggregation of lifestyle behaviours and their association with metabolic-associated fatty liver disease (MAFLD) remain unclear. We identified lifestyle patterns and investigated their association with the risk of developing MAFLD in a sample of Chinese adults who underwent annual physical examinations. METHODS: Annual physical examination data of Chinese adults from January 2016 to December 2020 were used in this study. We created a scoring system for lifestyle items combining a statistical method (multivariate analysis of variance) and clinical expertise (Delphi method). Subsequently, principal component analysis and two-step cluster analysis were implemented to derive the lifestyle patterns of men and women. Binary logistic regression analysis was used to explore the prevalence risk of MAFLD among lifestyle patterns stratified by sex. RESULTS: A total of 196,515 subjects were included in the analysis. Based on the defined lifestyle scoring system, nine and four lifestyle patterns were identified for men and women, respectively, which included "healthy or unhealthy" patterns and mixed patterns containing a combination of healthy and risky lifestyle behaviours. This study showed that subjects with an unhealthy or mixed pattern had a significantly higher risk of developing MAFLD than subjects with a relatively healthy pattern, especially among men. CONCLUSIONS: Clusters of unfavourable behaviours are more prominent in men than in women. Lifestyle patterns, as important factors influencing the development of MAFLD, show significant sex differences in the risk of MAFLD. There is a strong need for future research to develop targeted MAFLD interventions based on the identified behavioural clusters by sex stratification.

On the Relationship between ANOVA Main Effects and Average Treatment Effects.

We adopt a causal inference perspective to shed light into which ANOVA type of sums of squares (SS) should be used for testing main effects and whether main effects should be considered at all in the presence of interactions. We consider balanced, proportional and nonorthogonal designs, and models with and without interactions. When the design is balanced, we show that the average treatment effect is estimated by the main effects obtained by type I, II, and III sums of squares. In proportional designs, we show that the average treatment effect is estimated by the the type I and type II main effects, whereas type III SS yield biased estimates of the average treatment effect if there are interactions. When the design is nonorthogonal, ANOVA type I is always highly biased and ANOVA type II and III main effects are biased if there are interactions. We include a simulation study to illustrate the magnitude of the bias in estimating the average treatment effect across a variety of conditions, and provide recommendations for applied researchers.

Automatic Life Detection Based on Efficient Features of Ground-Penetrating Rescue Radar Signals.

Good feature engineering is a prerequisite for accurate classification, especially in challenging scenarios such as detecting the breathing of living persons trapped under building rubble using bioradar. Unlike monitoring patients' breathing through the air, the measuring conditions of a rescue bioradar are very complex. The ultimate goal of search and rescue is to determine the presence of a living person, which requires extracting representative features that can distinguish measurements with the presence of a person and without. To address this challenge, we conducted a bioradar test scenario under laboratory conditions and decomposed the radar signal into different range intervals to derive multiple virtual scenes from the real one. We then extracted physical and statistical quantitative features that represent a measurement, aiming to find those features that are robust to the complexity of rescue-radar measuring conditions, including different rubble sites, breathing rates, signal strengths, and short-duration disturbances. To this end, we utilized two methods, Analysis of Variance (ANOVA), and Minimum Redundancy Maximum Relevance (MRMR), to analyze the significance of the extracted features. We then trained the classification model using a linear kernel support vector machine (SVM). As the main result of this work, we identified an optimal feature set of four features based on the feature ranking and the improvement in the classification accuracy of the SVM model. These four features are related to four different physical quantities and independent from different rubble sites.

Reduced tillage and crop diversification can improve productivity and profitability of rice-based rotations of the Eastern Gangetic Plains.

Intensive rice (Oryza sativa)-based cropping systems in south Asia provide much of the calorie and protein requirements of low to middle-income rural and urban populations. Intensive tillage practices demand more resources, damage soil quality, and reduce crop yields and profit margins. Crop diversification along with conservation agriculture (CA)-based management practices may reduce external input use, improve resource-use efficiency, and increase the productivity and profitability of intensive cropping systems. A field study was conducted on loamy soil in a sub-tropical climate in northern Bangladesh to evaluate the effects of three tillage options and six rice-based cropping sequences on grain, calorie, and protein yields and gross margins (GM) for different crops and cropping sequences. The three tillage options were: (1) conservation agriculture (CA) with all crops in sequences untilled, (2) alternating tillage (AT) with the monsoon season rice crop tilled but winter season crops untilled, and (3) conventional tillage (CT) with all crops in sequences tilled. The six cropping sequences were: rice-rice (R-R), rice-mung bean (Vigna radiata) (R-MB), rice-wheat (Triticum aestivum) (R-W), rice-maize (Zea mays) (R-M), rice-wheat-mung bean (R-W-MB), and rice-maize-mung bean (R-M-MB). Over three years of experimentation, the average monsoon rice yield was 8% lower for CA than CT, but the average winter crops yield was 13% higher for CA than CT. Systems rice equivalent yield (SREY) and systems calorie and protein yields were about 5%, 3% and 6%, respectively, higher under CA than CT; additionally, AT added approximately 1% more to these benefits. The systems productivity gain under CA and AT resulted in higher GM by 16% while reducing the labor and total production cost under CA than CT. The R-M rotation had higher SREY, calorie, protein yields, and GM by 24%, 26%, 66%, and 148%, respectively, than the predominantly practiced R-R rotation. The R-W-MB rotation had the highest SREY (30%) and second highest (118%) GM. Considering the combined effect of tillage and cropping system, CA with R-M rotation showed superior performance in terms of SREY, protein yield, and GM. The distribution of labor use and GM across rotations was grouped into four categories: R-W in low-low (low labor use and low GM), R-M in low-high (low labor use and high GM), R-W-MB and R-M-MB in high-high (high labor use and high GM) and R-R and R-MB in high-low (high labor use and low GM). In conclusion, CA performed better than CT in different winter crops and cropping systems but not in monsoon rice. Our results demonstrate the multiple benefits of partial and full CA-based tillage practices employed with appropriate crop diversification to achieve sustainable food security with greater calorie and protein intake while maximizing farm profitability of intensive rice-based rotational systems.

Extraction of Bioactive Compounds from C. vulgaris Biomass Using Deep Eutectic Solvents.

C. vulgaris microalgae biomass was employed for the extraction of valuable bioactive compounds with deep eutectic-based solvents (DESs). Particularly, the Choline Chloride (ChCl) based DESs, ChCl:1,2 butanediol (1:4), ChCl:ethylene glycol (1:2), and ChCl:glycerol (1:2) mixed with water at 70/30 w/w ratio were used for that purpose. The extracts' total carotenoid (TCC) and phenolic contents (TPC), as well as their antioxidant activity (IC50), were determined within the process of identification of the most efficient solvent. This screening procedure revealed ChCl:1,2 butanediol (1:4)/H2O 70/30 w/w as the most compelling solvent; thus, it was employed thereafter for the extraction process optimization. Three extraction parameters, i.e., solvent-to-biomass ratio, temperature, and time were studied regarding their impact on the extract's TCC, TPC, and IC50. For the experimental design and process optimization, the statistical tool Response Surface Methodology was used. The resulting models' predictive capacity was confirmed experimentally by carrying out two additional extractions under conditions different from the experimental design.

Mathematical Modelling and Optimization for Facile Synthesis of Structured Activated Carbon (ACs) from Adansonia kilima (Baobab) Wood Chips Integrating Microwave-Assisted Pyrolysis for the Elimination of Lead (II) Cations from Wastewater Effluents.

In this research, activated carbon (AC) was synthesized from ligno-cellulosic residues of Adansonia kilima (Baobab) wood chips (AKTW) using two-step semi-carbonization and subsequent pyrolysis using microwave-induced heating (MWP) in the presence of a mild activating agent of K2CO3. The influence of process input variables of microwave power (x1), residence time (y1), and amount of K2CO3 (z1) were analysed to yield superior quality carbon having maximum removal efficiencies (R1) for lead (II) cations from waste effluents, fixed carbon percentages (R2), and carbon yield percentages (R3). Analysis of variance (ANOVA) was used to develop relevant mathematical models, with an appropriate statistical assessment of errors. Level factorial response surface methodology (RSM) relying on the Box-Behnken design (BBD) was implemented for the experimental design. The surface area and porous texture of the samples were determined using Brunauer, Emmett, and Teller (BET) adsorption/desorption curves based on the N2 isotherm. Surface morphological structure was observed using field emission scanning electron microscopic (FESEM) analysis. Thermogravimetric analysis (TGA) was carried out to observe the thermal stability of the sample. Change in the carbon content of the samples was determined using ultimate analysis. X-ray diffraction (XRD) analysis was performed to observe the crystalline and amorphous texture of the samples. The retention of a higher proportion of fixed carbon (80.01%) ensures that the synthesized adsorbent (AKTWAC) will have a greater adsorption capacity while avoiding unwanted catalytic activity for our synthesized final sample.

Blood pressure patterns of hypertensive disorders of pregnancy in first and second trimester and contributing factors: a retrospective study.

We aimed to investigate the blood pressure (BP) patterns of hypertensive disorders of pregnancy (HDP) in the first and second trimesters and its contributing factors, which may help us understand its pathogenesis and identify this group of diseases in a timely manner. SPSS 21.0 was used to describe the BP patterns of 688 HDP as well as 2050 normotensive pregnancies respectively before 28 gestational weeks, and the repeated measurements and two-way ANOVA was used to decide the significant difference of blood pressure in the same period. The results revealed blood pressure in HDP underwent a mid-pregnancy drop as normal while the drop was unremarkable in advanced-age or obesity pregnancies. Besides, we found blood pressure was significantly higher in patients during first and second trimesters, not just after 20 weeks. In conclusion, our study indicated a significant elevation of blood pressure had appeared before 20 weeks in HDP pregnancies, we should pay more attention to monitoring blood pressure before 20 weeks, especially for advanced age and obese women.IMPACT STATEMENTWhat is already known on this subject? Gestational hypertension, preeclampsia as well as eclampsia were supposed to have the similar pathogenesis and their time of onset was strictly defined after 20 gestational weeks, while the reason for the time point was not clear. On the other hand, higher blood pressure in the first trimester was associated with increasing risk of developing HDP, while the blood pressure(BP) pattern of normal as well as HDP pregnancy was still controversial, especially for the existence of mid-trimester drop.What do the results of this study add? Firstly, we found blood pressure in HDP underwent a mid-pregnancy drop as normal while the BP drop was unremarkable in advanced-age or obesity pregnancies. Secondly, we noticed the blood pressure in HDP was significantly higher than the normal before 20 weeks, which had not been proved before.What are the implications of these findings for clinical practice and/or further research? On one hand, both the abnormal elevation of BP and the development of the placenta happened in the first trimester suggested toxic substances caused by the defective placenta played a vital role in the onset and aggravation of HDP, which guides us to pay more attention to monitor blood pressure before 20 weeks, especially for advanced age and obesity pregnancies. On the other hand, our results about BP patterns in HDP help us identify this group of diseases in time which can contribute to a better outcome.

Legibility of 3D printed typography at smaller sizes.

The aim of the research was to investigate the usability and legibility of 3D printed typeface characters in smaller sizes. In the experimental investigation two software programs for letter modelling, three typefaces, three type sizes, two weight options, and two printing materials were tested. The samples were analysed visually and with image analysis. The legibility tests were conducted in laboratory conditions and testing chamber. The participants were asked to read pangrams and answer close-ended questions. The reading speed and understanding of the text were measured and analysed. It was found that the success of printing parts of letters, as well as their recognition and visual evaluation, is most often influenced by two analysed factors, i.e. weight option and type size, in all three typefaces. We established that the type size is statistically significant, and that the typographic tonal density is influenced by the typeface and the material used.Practitioner summary: The research presents the investigation of usability and legibility of 3D printed typefaces at smaller sizes. Five variables were analysed visually and with image analysis. Typographic tonal density, reading speed, and text comprehension were evaluated. The findings demonstrated that weight option, type size, and material influence the reading speed and text comprehension.HIGHLIGHTSFive different parameters were investigated regarding usability of 3D printed typeface.Legibility of different typefaces, weight options, and type sizes were tested.Typographic tonal density was evaluated with image analysis.Print quality of different materials using an FDM technology printer was evaluated.

Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants.

Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO2, and SO2). This study uses the Jinan Central District as the study area and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) the effects of most of spatial distribution indexes of building on the concentration distribution of the four gaseous pollutants were significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations. (2) The spatial distribution of CO and NO2 is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO2 concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO2 concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.

A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks.

The unexpected emergence of the COVID-19 pandemic has changed how grocery shopping is done. The grocery retail stores need to ensure hygiene, quality, and safety concerns in-store shopping by providing "no-touch" smart packaging solutions for agri-food products. The benefit of smart packaging is to inform consumers about the freshness level of a packaged product without having direct contact. This paper proposes a data-driven decision support system that uses smart packaging as a smart product-service system to manage the sustainable grocery store supply chain during outbreaks to prevent food waste. The proposed model dynamically updates the price of a packaged perishable product depending on freshness level while reducing food waste and the number of rejected customers and maximising profit by increasing the inventory turnover rate of grocery stores. The model was tested on a hypothetical but realistic case study of a single product. The results of this study showed that stock capacities, freshness discount rate, freshness period, and quantity discounts significantly affect the performance of a grocery store supply chain during outbreaks.

Design and analysis of water quality monitoring and filtration system for different types of water in Malaysia.

Water pollution always occurs in Malaysia due to industrial, construction, agriculture, and household activities. River pollution can disturb water supply that eventually affects business and household activities. Thus, water quality monitoring system is needed to detect contaminated water. We developed a water quality monitoring and filtration system controlled by Arduino. The proposed system was designed in Proteus software and ThingSpeak platform was used for real-time monitoring. The main objective of the study was to compare water quality of river, lake and tap water in terms of pH, temperature, turbidity, electrical conductivity and oxidation-reduction potential. If the water quality was not satisfied, the water sample would be filtered through filtration system. Water turbidity level, pH, temperature, electrical conductivity, and oxidation-reduction potential for filtered and nonfiltered water were compared and analyzed according to international and national water quality standard. Besides that, statistical analysis such as box plot and one-way analysis of variance test was applied to validate data from the system. The real-time water quality monitoring system was implemented through data storage, data transfer, and data processing. The system was connected to wireless fidelity whereas the output data was sent to the user and monitored by ThingSpeak. The system can be further upgraded and scaled up to be applied in the main tank at our home or factory. The outcome of this research can be used as a reference for further study on lake and river pollution monitoring system. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04192-x.

A semiparametric model for between-subject attributes: Applications to beta-diversity of microbiome data.

The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high-throughput sequencing, with highly similar sequences binned together, we obtain operational taxonomic units (OTUs) profiles for each subject. Due to the high-dimensionality and nonnormality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance-based beta-diversity between individuals. Analyses of such between-subject attributes are not amenable to the predominant within-subject-based statistical paradigm, such as t-tests and linear regression. In this paper, we propose a new approach to model beta-diversity as a response within a regression setting by utilizing the functional response models (FRMs), a class of semiparametric models for between- as well as within-subject attributes. The new approach not only addresses limitations of current methods for beta-diversity with cross-sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data.

Hospital-acquired complications: the relative importance of hospital- and patient-related factors.

OBJECTIVE: To quantify the prevalence of hospital-acquired complications; to determine the relative influence of patient- and hospital-related factors on complication rates. DESIGN, PARTICIPANTS: Retrospective analysis of administrative data (Integrated South Australian Activity Collection; Victorian Admitted Episodes Dataset) for multiple-day acute care episodes for adults in public hospitals. SETTING: Thirty-eight major public hospitals in South Australia and Victoria, 2015-2018. MAIN OUTCOME MEASURES: Hospital-acquired complication rates, overall and by complication class, by hospital and hospital type (tertiary referral, major metropolitan service, major regional service); variance in rates (intra-class correlation coefficient, ICC) at the patient, hospital, and hospital type levels as surrogate measures of their influence on rates. RESULTS: Of 1 558 978 public hospital episodes (10 029 918 bed-days), 151 486 included a total of 214 286 hospital-acquired complications (9.72 [95% CI, 9.67-9.77] events per 100 episodes; 2.14 [95% CI, 2.13-2.15] events per 100 bed-days). Complication rates were highest in tertiary referral hospitals (12.7 [95% CI, 12.6-12.8] events per 100 episodes) and for episodes including intensive care components (37.1 [95% CI, 36.7-37.4] events per 100 episodes). For all complication classes, inter-hospital variation was determined more by patient factors (overall ICC, 0.55; 95% CI, 0.53-0.57) than by hospital factors (ICC, 0.04; 95% CI, 0.02-0.07) or hospital type (ICC, 0.01; 95% CI, 0.001-0.03). CONCLUSIONS: Hospital-acquired complications were recorded for 9.7% of hospital episodes, but patient-related factors played a greater role in determining their prevalence than the treating hospital.