Statistical Modeling Approach Research Articles

Identifying regional disparities of infant mortality rates in Bangladesh: insights from nationwide cross-sectional studies and a statistical modelling approach using linear mixed effects model with temporal variability

ObjectiveThe major objective of this project is to find the best suitable model for district-wise infant mortality rate (IMR) data of Bangladesh over the period 2014–2020 that captures the regional...

Analysis of asymmetrical supercapacitor with horizontally configured electrodes

Energy is available in several forms but its use to humans is predominantly in the electrical form. Since electrical energy is not immediately consumed post conversion, energy storage devices are required for its storage. This reinforces the importance of research in electrical energy storage devices. Till date, very few energy storage devices have been developed for commercial use. Asymmetrical supercapacitors are the newest addition to this list and have the potential to replace conventional energy storage devices in times to come. They can be further classified based on their electrode orientation namely horizontally and vertically configured electrodes. Research has been conducted on asymmetrical supercapacitors with horizontally configured electrodes as they have a better performance over vertically configured electrode. Analysis of asymmetrical supercapacitor with horizontally configured electrodes through statistical modelling approach is presented in the paper which is useful in setting electrode parameter values during the manufacture of this device. Electrode material loading on current collector of both electrodes is found to be affecting specific-capacitance values; they also add to nonlinearity in the device. Metal oxide contents are an addition to the interaction effect. Modelling approach adopted has provided better insights as to how various electrode parameters interact and in turn affect the specific capacitance. Presented research work also brings out need of extra negative plate or bigger size negative plate in asymmetrical supercapacitors. Findings of this research work can be used as basis for study of other electrical energy storage devices such as battery, fuel cell etc.

Modelling the transmission of dengue, zika and chikungunya: a scoping review protocol

IntroductionAedes mosquitoes are the primary vectors for the spread of viruses like dengue (DENV), zika (ZIKV) and chikungunya (CHIKV), all of which affect humans. Those diseases contribute to global public...

ZPerf: A Statistical Gray-Box Approach to Performance Modeling and Extrapolation for Scientific Lossy Compression

As simulation-based scientific discovery is being further scaled up on high-performance computing systems, the disparity between compute and I/O has continued to widen. As such, domain scientists are forced to save only a small portion of their simulation data to persistent storage, and as a consequence, important physics may be discarded for data analysis. Error-bounded lossy compression has made tremendous progress recently in bridging the gap between compute and I/O, and played an increasingly important role in science productions. Nevertheless, a key hurdle to the wide adoption of lossy compression is the lack of understanding of compression performance, which is the result of the complex interaction between data, error bound, and compression algorithm. In this work, we present zPerf, a statistical gray-box performance modeling approach for scientific lossy compression. Our contributions are threefold: 1) We develop zPerf to estimate the performance of prediction-based and transform-based lossy compression techniques, based on in-depth understanding and statistical modeling for data features and core compression metrics; 2) We demonstrate the in-detailed implementation of zPerf using two case studies, where we derive the performance modeling for SZ and ZFP, two leading lossy compressors; 3) We evaluate the effectiveness of zPerf on real-world datasets across various domains. Based on the evaluation, we demonstrate the efficacy of zPerf performance model; 4) We further discuss three case studies where zPerf is applied to extrapolate the compression ratio of SZ and ZFP with alternative encoding schemes as well as ZFP with an alternative transform scheme. Through the case studies, we demonstrate the potential of zPerf for exploring the design space of lossy compression, which has hardly been studied in the literature

An assessment of Sentinel‐1 synthetic aperture radar, geophysical and topographical covariates for estimating topsoil particle‐size fractions

AbstractData derived from synthetic aperture radar (SAR) are widely employed to predict soil properties, particularly soil moisture and soil carbon content. However, few studies address the use of microwave sensors for soil texture retrieval and those that do are typically constrained to bare soil conditions. Here, we test two statistical modelling approaches—linear (with and without interaction terms) and tree‐based models, namely compositional linear regression model (LRM) and random forest (RF)—and both nongeophysical (e.g., surface soil moisture, topographic, etc) and geophysical‐based (electromagnetic, magnetic and radiometric) covariates to estimate soil texture (sand %, silt % and clay %), using microwave remote sensing data (ESA Sentinel‐1). The statistical models evaluated explicitly consider the compositional nature of soil texture and were evaluated with leave‐one‐out cross‐validation (LOOCV). Our findings indicate that both modelling approaches yielded better estimates when fitted without the geophysical covariates. Based on the Nash–Sutcliffe efficiency coefficient (NSE), LRM slightly outperformed RF, with NSE values for sand, silt and clay of 0.94, 0.62 and 0.46, respectively; for RF, the NSE values were 0.93, 0.59 and 0.44. When interaction terms were included, RF was found to outperform LRM. The inclusion of interactions in the LRM resulted in a decrease in NSE value and an increase in the size of the residuals. Findings also indicate that the use of radar‐derived variables (e.g., VV, VH, RVI) alone was not able to predict soil particle size without the aid of other covariates. Our findings highlight the importance of explicitly considering the compositional nature of soil texture information in statistical analysis and regression modelling. As part of the continued assessment of microwave remote sensing data (e.g., ESA Sentinel‐1) for predicting topsoil particle size, we intend to test surface scattering information derived from the dual‐polarimetric decomposition technique and integrate that predictor into the models in order to deal with the effects of vegetation cover on topsoil backscattering.

Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool.

The time-varying reproduction number (Rt) is an important measure of epidemic transmissibility that directly informs policy decisions and the optimisation of control measures. EpiEstim is a widely used opensource software tool that uses case incidence and the serial interval (SI, time between symptoms in a case and their infector) to estimate Rt in real-time. The incidence and the SI distribution must be provided at the same temporal resolution, which can limit the applicability of EpiEstim and other similar methods, e.g. for contexts where the time window of incidence reporting is longer than the mean SI. In the EpiEstim R package, we implement an expectation-maximisation algorithm to reconstruct daily incidence from temporally aggregated data, from which Rt can then be estimated. We assess the validity of our method using an extensive simulation study and apply it to COVID-19 and influenza data. For all datasets, the influence of intra-weekly variability in reported data was mitigated by using aggregated weekly data. Rt estimated on weekly sliding windows using incidence reconstructed from weekly data was strongly correlated with estimates from the original daily data. The simulation study revealed that Rt was well estimated in all scenarios and regardless of the temporal aggregation of the data. In the presence of weekend effects, Rt estimates from reconstructed data were more successful at recovering the true value of Rt than those obtained from reported daily data. These results show that this novel method allows Rt to be successfully recovered from aggregated data using a simple approach with very few data requirements. Additionally, by removing administrative noise when daily incidence data are reconstructed, the accuracy of Rt estimates can be improved.

Statistical modeling and dependence analysis for tide level via multivariate extreme value distribution method

This paper presents a statistical modeling approach to explore the dependence of extreme values in multi-site tidal water levels (TL) using hourly data from six tidal stations in the Kansai region, Japan. The proposed method utilizes a multi-site conditional extreme value model (CEVM) and a peak over threshold (POT) model with yearly order grouping (YOG) to handle extreme value dependence and marginal modeling of tidal CEVM, respectively. The results demonstrate the effectiveness of the proposed approach in analyzing extreme value dependence among multiple sites, with the YOG method yielding favorable results for extreme value dependence analysis. The extreme dependence effect of TL and the probability of exceedance at multiple sites under extreme conditions are investigated. The paper concludes that the extreme dependence effect should be captured through a multivariate CEVM and provides a more accurate basis for engineering construction and disaster prevention. The novelty of this work lies in its statistical modeling approach that enables accurate multi-site extreme value analysis and provides a more comprehensive understanding of the extreme value dependence of the TLs, which has significant implications for disaster management and prevention.

Spatially variable effects of streamflow on water temperature and thermal sensitivity within a salmon‐bearing watershed in interior British Columbia, Canada

AbstractWarming temperatures can have negative consequences for aquatic organisms, especially cold‐adapted fishes such as Pacific salmon. The magnitude of warming is related to the thermal sensitivity of streams in salmon‐bearing watersheds (i.e., change in stream temperature for every 1°C increase in air temperature), which can vary based on several factors including streamflow. Management actions to increase streamflow may therefore benefit salmon by decreasing thermal sensitivity. However, the effects of streamflow on thermal sensitivity are often complex, as the temperature of flow inputs can directly increase or decrease temperatures. This study aimed to disentangle the influence of streamflow on thermal sensitivity and stream temperature over 4 years in the Nicola River, a regulated semiarid watershed in south‐central British Columbia, Canada. A statistical modeling approach was used to estimate streamflow effects on stream temperatures and thermal sensitivity (i.e., relationship of regional air temperature to stream temperature) at 12 sites from 2018 to 2021. Streamflow had a variable influence on stream temperatures across the watershed via both direct effects and by modulating thermal sensitivity. At a given site, streamflow was generally negatively associated with summer daily mean stream temperature, but the magnitude of its influence varied among locations and years. The influence of streamflow on thermal sensitivity was also highly variable both spatially and temporally. The analysis suggests that there may be complex relationships between streamflow, stream temperature, and thermal sensitivity, which complicates the efficacy of flow as a lever to mitigate high temperatures in regulated systems.

A framework for modelling spatio-temporal trends in crop production using generalised additive models

Satellite imagery provides opportunities for inference of trends in crop production across space and time. However, the large size of these datasets has made application of statistical modelling approaches computationally difficult. Recent advances in computational techniques and infrastructure have allowed generalised additive models to be fitted to very large datasets. We propose a framework for inferring trends in crop production across space and time using generalised additive models which accounts for inter-annual trends (main effect of year), spatial distribution (main effect of space), crop ontogeny (main effect of month), inter-annual changes in seasonality (interaction between year and month), and inter-annual changes in spatial distribution (interaction between year and space). Application of the proposed model to farm scale, multi-field sites in the Ord River Irrigation Area, Western Australia, demonstrates that this approach is able to decompose variation into the aforementioned effects. Furthermore, comparison of grain production observations and estimates for the Western Australian Wheatbelt as ground-truth data showed agreement with inferences drawn from the proposed model, with prediction terms for the main effect of year positively correlated with estimated tonnes produced from 2013 to 2021 (p = 0.03). Finally, application to Madagascar, which has been experiencing a food crisis, revealed a decreasing trend in cropland Normalised Difference Vegetation Index (NDVI) from 2014 to 2021 of 2.5%, raising concerns about ongoing food security. The proposed modelling framework is adaptable to numerous agricultural research problems.

Predicting the Compressive Strength of Green Concrete at Various Temperature Ranges Using Different Soft Computing Techniques

To overcome the environmental impact of cement production in concrete, the construction industry is adopting eco-friendly approaches, such as incorporating alternative and recycled materials and minimizing carbon emissions in concrete production. One such material that has gained prominence is ground granulated blast furnace slag (GGBFS). This study focuses on investigating the compressive strength of concrete at 28 days of age by examining the influences of several factors, such as temperature, water-to-binder ratio (w/b), GGBFS-to-binder ratio (GGBFS/b), fine aggregate, coarse aggregate, and superplasticizer. A statistical modeling approach was employed to comprehensively analyze these parameters and assess their impact on compressive strength. To accomplish this, the study collected and analyzed data from the literature, resulting in a dataset of 210 observations. The dataset was divided into training and testing groups, and statistical analyses were performed to assess the relationships between the input parameters and compressive strength. The correlation analysis revealed insignificant relationships between the input parameters and compressive strength, indicating that multiple factors affect strength. Different models were employed to predict compressive strength, such as linear regression, nonlinear regression, quadratic, full quadratic models, and artificial neural networks (ANN). The findings of this study contribute to a better understanding of the factors that influence the compressive strength of concrete containing GGBFS. The results underscore the importance of considering multiple parameters to predict strength accurately.

Does pain duration and other variables measured at baseline predict re-referral of low back pain patients managed on an evidence-based pathway? A cohort study

ObjectiveTo explore the association between baseline pain duration and the likelihood of re-referral of patients with low back pain (LBP) managed on the evidence-based North East of England Regional Back Pain and Radicular Pain Pathway (NERBPP). Study designLongitudinal, observational cohort study. MethodsIn all, 12,509 adults with LBP were identified as having been discharged from the pathway, between May 2015 and December 2019. To quantify any association between baseline pain duration and the likelihood of re-referral, two statistical modelling approaches, were used: logistic regression models for odds ratios and generalised linear models with a binomial link function in order to quantify risk differences. ResultsTwenty-five percent of patients with LBP, who were discharged, re-referred for management over a 4.5-year period. A large difference in pain duration of 2 SD days was statistically associated with re-referral, with an odds ratio of 1.22 (95% CI: 1.03, 1.44) and a risk difference of 3.6% (95% CI: 0.6, 6.6). Nevertheless, the predictive value of an individual’s pain duration was found to be weak for re-referral. Higher baseline disability [odds ratio of 1.40 (95% CI: 1.07, 1.83)] and a younger age at baseline [odds ratio of 0.73 (95% CI 0.61, 0.86)] were also associated with an increased risk of re-referral. ConclusionsBaseline pain duration, disability and younger age are statistically associated with re-referral onto the NERBPP. However, the value of these variables for predicting an individual’s risk of re-referral is weak. Contribution of paper•The NERBPP is an evidence-based clinical pathway for low back pain and a forerunner to the National Back Pain Pathway.•Baseline pain duration, disability and younger participants have an association with re-referral onto the NERBPP, however, the predictive values of these variables are weak.•25% of patients with low back pain, who were discharged, re-referred for management over a four-and-a-half-year period, re-referral rates onto the NERBPP have not previously been published.

The unpolarized TMD valence quark distributions of light mesons in the statistical covariant parton model approach

We investigate the unpolarized transverse momentum dependent (TMD) structure of light mesons at low Q^2 scales in nonperturbative region of quantum chromodynamics (QCD). To this end, we calculate the unpolarized TMD valence quark distribution functions of pion and kaon based on the covariant parton model in which the flavour dependent covariant momentum distribution of quarks is needed. We apply the statistical model for this momentum distribution, for the first time, and also consider the quarks to be massive. The results of TMD valence distributions of light mesons show convenient general properties. We also obtain the integrated valence quark distributions of pion and kaon and compare our results with those of other models.

Rapid monitoring of cropland primary productivity and shipping activity in Ukraine.

Ukraine is an important global exporter of grain, especially to several countries with vulnerable food systems. The war in Ukraine may disrupt global food supply by limiting the planting, growth, and harvest of crops, or disrupting grain supply logistics. We apply a novel statistical modelling approach to satellite images of cropland in Ukraine for fast inference and exploration of cropping patterns and their influences in challenging environments. We also present satellite-derived cargo shipping activity as an accompaniment to these outputs to better explore the outcomes. Cropland Gross Primary Productivity in 2022 was 0.25gC/m2 lower than the 2010-2021 baseline period (p < 0.001). Similarly, cumulative annual cargo shipping activity ports in the Odesa and Mariupol regions were 45% and 62% lower in 2022 than in 2021, respectively. This suggests that cropland primary productivity has suffered during the conflict, and reliance on a few key port areas introduces vulnerability to the value chain.

Prenatal lead exposure is negatively associated with the gut microbiome in childhood.

Metal exposures are associated with gut microbiome (GM) composition and function, and exposures early in development may be particularly important. Considering the role of the GM in association with many adverse health outcomes, understanding the relationship between prenatal metal exposures and the GM is critically important. However, there is sparse knowledge of the association between prenatal metal exposure and GM later in childhood. This analysis aims to identify associations between prenatal lead (Pb) exposure and GM composition and function in children 9-11 years old. Data come from the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) cohort based in Mexico City, Mexico. Prenatal metal concentrations were measured in maternal whole blood drawn during the second and third trimesters of pregnancy. Stool samples collected at 9-11 years old underwent metagenomic sequencing to assess the GM. This analysis uses multiple statistical modeling approaches, including linear regression, permutational analysis of variance, weighted quantile sum regression (WQS), and individual taxa regressions, to estimate the association between maternal blood Pb during pregnancy and multiple aspects of the child GM at 9-11 years old, adjusting for relevant confounders. Of the 123 child participants in this pilot data analysis, 74 were male and 49 were female. Mean prenatal maternal blood Pb was 33.6 (SE = 2.1) ug/L and 34.9 (SE = 2.1) ug/L at second and third trimesters, respectively. Analysis suggests a consistent negative relationship between prenatal maternal blood Pb and the GM at age 9-11, including measures of alpha and beta diversity, microbiome mixture analysis, and individual taxa. The WQS analysis showed a negative association between prenatal Pb exposure and the gut microbiome, for both second and third trimester exposures (2Tβ = -0.17, 95%CI = [-0.46,0.11]; 3Tβ = -0.17, 95%CI = [-0.44,0.10]). Ruminococcus gnavus, Bifidobacterium longum, Alistipes indistinctus, Bacteroides caccae, and Bifidobacterium bifidum all had weights above the importance threshold from 80% or more of the WQS repeated holdouts in association with both second and third trimester Pb exposure. Pilot data analysis suggests a negative association between prenatal Pb exposure and the gut microbiome later in childhood; however, additional investigation is needed.

A statistical modeling approach based on the small-scale field trial and meteorological data for preliminary prediction of the impact of low temperature on Eucalyptus globulus trees

Eucalyptus trees are important for industrial forestry plantations because of their high potential for biomass production, but their susceptibility to damage at low temperatures restricts their plantation areas. In this study, a 6-year field trial of Eucalyptus globulus was conducted in Tsukuba, Japan, which is the northernmost reach of Eucalyptus plantations, and leaf damage was quantitatively monitored over four of six winters. Leaf photosynthetic quantum yield (QY) levels, an indicator of cold stress-induced damage, fluctuated synchronously with temperature in the winters. We performed a maximum likelihood estimation of the regression model explaining leaf QY using training data subsets for the first 3 years. The resulting model explained QY by the number of days when the daily maximum temperature was below 9.5 °C over approximately the last 7 weeks as an explanatory variable. The correlation coefficient and coefficient of determination of prediction by the model between the predicted and observed values were 0.84 and 0.70, respectively. The model was then used to perform two kinds of simulations. Geographical simulations of potential Eucalyptus plantation areas using global meteorological data from more than 5,000 locations around the world successfully predicted an area that generally agreed with the global Eucalyptus plantation distribution reported previously. Another simulation based on meteorological data of the past 70 years suggested that global warming will increase the potential E. globulus plantation area in Japan approximately 1.5-fold over the next 70 years. These results suggest that the model developed herein would be applicable to preliminary predictions of E. globulus cold damage in the field.

Effect of process parameters on performances measured in filament and pellets-based additively manufactured parts

Among all the additive manufacturing (AM) technologies, the material extrusion-based AM is the most popular, and widely used for prototyping, rapid tooling, and direct part production in the current scenario. In material extrusion AM (MEAM), a solid form of material is converted to a semi-molten form and extruded out of a nozzle to deposit in a layer-by-layer manner. Nowadays MEAM three-dimensional (3D) printers are very popular among different application sectors. This motivates us to develop various commercial as well as open-source entry-level 3D printers such as the development of filament extruder 3D printers, pellet extruder 3D printer, or a combination of both. However, to develop or operate these printers, preliminary knowledge of various extrusion parameters is required to obtain the desired quality in the fabricated parts. Therefore, an initial attempt has been made to provide information related to various extrusion parameters such as definition, working range, and the effect of these parameters on performance measured. The impact of process parameters on the performance of either filament extrusion AM or pellet/granules extrusion AM has only been investigated in the studies available in the literature. However, this study provides a comprehensive review of the effects of process parameters on performance measurements for both types of extrusion-based AM processes. In this research work, along with a detailed review of previous works, a study of various process parameters, modeling, and methodology has been provided. The statistical modeling approach, histogram normal plots have been provided along with mean, standard deviation, and percentage to decide the optimum process parameters based on data available in the existing literature.

Novel Baseline Facial Muscle Database Using Statistical Shape Modeling and In Silico Trials toward Decision Support for Facial Rehabilitation.

Backgrounds and Objective: Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed. However, there is a lack of facial muscle baseline data to evaluate the patient states and guide as well as optimize the rehabilitation strategy. In this present study, we aimed to develop a novel baseline facial muscle database (static and dynamic behaviors) using the coupling between statistical shape modeling and in-silico trial approaches. Methods: 10,000 virtual subjects (5000 males and 5000 females) were generated from a statistical shape modeling (SSM) head model. Skull and muscle networks were defined so that they statistically fit with the head shapes. Two standard mimics: smiling and kissing were generated. The muscle strains of the lengths in neutral and mimic positions were computed and recorded thanks to the muscle insertion and attachment points on the animated head and skull meshes. For validation, five head and skull meshes were reconstructed from the five computed tomography (CT) image sets. Skull and muscle networks were then predicted from the reconstructed head meshes. The predicted skull meshes were compared with the reconstructed skull meshes based on the mesh-to-mesh distance metrics. The predicted muscle lengths were also compared with those manually defined on the reconstructed head and skull meshes. Moreover, the computed muscle lengths and strains were compared with those in our previous studies and the literature. Results: The skull prediction's median deviations from the CT-based models were 2.2236 mm, 2.1371 mm, and 2.1277 mm for the skull shape, skull mesh, and muscle attachment point regions, respectively. The median deviation of the muscle lengths was 4.8940 mm. The computed muscle strains were compatible with the reported values in our previous Kinect-based method and the literature. Conclusions: The development of our novel facial muscle database opens new avenues to accurately evaluate the facial muscle states of facial palsy patients. Based on the evaluated results, specific types of facial mimic rehabilitation exercises can also be selected optimally to train the target muscles. In perspective, the database of the computed muscle lengths and strains will be integrated into our available clinical decision support system for automatically detecting malfunctioning muscles and proposing patient-specific rehabilitation serious games.

Population modeling and housing demand prediction for the Saudi 2030 Vision: a case study of Riyadh City

PurposeThe Saudi 2030 Housing Program Vision aims to increase the population of Riyadh City, the capital of the Kingdom of Saudi Arabia, to between 15 and 20 million people. This paper aims to predict the demand for residential units in Riyadh City by 2030 in line with this vision.Design/methodology/approachThis paper adopts a statistical modeling approach to estimate the residential demands for Riyadh City. Several population growth models, including the nonlinear quadratic polynomial spline regression model, the sigmoidal logistic power model and the exponential model, are tested and applied to Riyadh to estimate the expected population in 2030. The growth model closest to the Kingdom’s goal of reaching between 15 and 20 million people in 2030 is selected, and the paper predicts the required number of residential units for the population obtained from the selected model. Desktop database research is conducted to obtain the data required for the modeling and analytical stage.FindingsThe exponential model predicts a population of 16,476,470 in Riyadh City by 2030, and as a result, 2,636,235 household units are needed. This number of housing units required in Riyadh City exceeds the available residential units by almost 1,370,000, representing 108% of the available residential units in Riyadh in 2020.Originality/valueThis study provides valuable insights into the demand for residential units in Riyadh City by 2030 in line with the Saudi 2030 Housing Program Vision, filling the gap in prior research. The findings suggest that significant efforts are required to meet the housing demand in Riyadh City by 2030, and policymakers and stakeholders need to take appropriate measures to address this issue.

Improved bacterial foraging optimization with deep learning based anomaly detection in smart cities

The Internet of Things (IoT) contains many smart devices that collect, store, communicate, and process data. IoT implementation has performed novel opportunities in industries, environments, businesses, and homes. Anomaly detection (AD) is helpful in IoT platforms that can recognize and prevent potential system failures, decrease downtime, enhance the quality of products and services, and improve overall operational efficacy. AD systems for IoT data contain statistical modelling, deep learning (DL), and machine learning (ML) approaches which detect patterns and anomalies in the data. This article introduces an Improved Bacterial Foraging Optimization with optimum deep learning for Anomaly Detection (IBFO-ODLAD) in the IoT network. The presented IBFO-ODLAD technique performs data normalization using Z-score normalization approach. For the feature selection process, the IBFO-ODLAD technique designs the IBFO algorithm to choose an optimal subset of features. In addition, the IBFO-ODLAD technique uses multiplicative long short term memory (MLSTM) model for intrusion detection and classification process. Furthermore, the Bayesian optimization algorithm (BOA) was executed for the optimum hyperparameter selection of the MLSTM model. The experimental outcome of the IBFO-ODLAD method was validated on the UNSW NB-15 dataset and UCI SECOM dataset. The experimental outcomes signified the improved performance of the IBFO-ODLAD algorithm with maximum accuracy of 98.89 % and 98.66 % validated on the UNSW NB-15 dataset and UCI SECOM dataset respectively.

Prevalence of opioid dependence in New South Wales, Australia, 2014-16: Indirect estimation from multiple data sources using a Bayesian approach.

To estimate the prevalence of, and number of unobserved people with opioid dependence by sex and age group in New South Wales (NSW), Australia. We applied a Bayesian statistical modelling approach to opioid agonist treatment records linked to adverse event rate data. We estimated prevalence from three types of adverse event separately: opioid mortality, opioid-poisoning hospitalizations and opioid-related charges. We extended the model and produced prevalence estimates from a 'multi-source' model based on all three types of adverse event data. This study was conducted in NSW, Australia, 2014-16 using data from the Opioid Agonist Treatment and Safety (OATS) study, which included all people who had received treatment for opioid dependence in NSW. Aggregate data were obtained on numbers of adverse events in NSW. Rates of each adverse event type within the OATS cohort were modelled. Population data were provided by State and Commonwealth agencies. Prevalence of opioid dependence among those aged 15-64 years in 2016 was estimated to be 0.96% (95% credible interval [CrI] = 0.82%, 1.12%) from the mortality model, 0.75% (95% CrI = 0.70%, 0.83%) from hospitalizations, 0.95% (95% CrI = 0.90%, 0.99%) from charges and 0.92% (95% CrI = 0.88%, 0.96%) from the multi-source model. Of the estimated 46 460 (95% CrI = 44 680, 48 410) people with opioid dependence in 2016 from the multi-source model, approximately one-third (16 750, 95% CrI = 14 960, 18 690) had no record of opioid agonist treatment within the last 4years. From the multi-source model, prevalence in 2016 was estimated to be 1.24% (95% CrI = 1.18%, 1.31%) in men aged 15-44, 1.22% (95% CrI = 1.14%, 1.31%) in men 45-64, 0.63% (95% CrI = 0.59%, 0.68%) in women aged 15-44 and 0.56% (95% CrI = 0.50%, 0.63%) in women aged 45-64. A Bayesian statistical approach to estimate prevalence from multiple adverse event types simultaneously calculates that the estimated prevalence of opioid dependence in NSW, Australia in 2016 was 0.92%, higher than previous estimates.