Direct Estimation Research Articles

Geospatial Data Aggregation Methods for Novel Geographies: Validating Congressional District Life Expectancy Estimates.

Place is a critical determinant of health. Recent novel analyses have explored health outcome estimation for small geographies, such as census tracts, as well as health outcome aggregation to geopolitical geographies with accountable political representatives, such as congressional districts. In one such application, combining these approaches, researchers aggregated census tract estimates of life expectancy to the congressional district level to derive local estimates, but such an approach has not been validated. Here, we compared two sources and approaches to calculating life expectancy data for Pennsylvania congressional districts. We used 2010-2015 census tract life expectancy estimates from the US Small-area Life Expectancy Estimates Project (LEEP) and dasymetric methods to compute population-weighted life expectancy aggregated to the congressional district level. Using georeferenced Vital Statistics data, we aggregated age-specific census tract death and population counts to congressional districts and used abridged life tables to estimate life expectancy. To validate the dasymetric aggregated estimates we compared absolute differences, assessed the correlation, and created Bland-Altman plots to visualize the agreement between the two measures. We found strong agreement between congressional district estimates of life expectancy at birth derived using the dasymetric LEEP model-based approach and Vital Statistics direct estimates approach, though life expectancy at older ages (75 and older) showed weak correlations. This validation contributes to our understanding of geospatial aggregation methods for novel geographies including congressional districts. Health outcome data aggregated to the congressional district geography can support congressional policy making aimed at improving population health outcomes.

Optimal charging of Li-ion batteries using sparse identification of nonlinear dynamics

Optimal charging of Li-ion batteries requires careful management of charge rates, as high rates can lead to accelerated degradation, while low rates significantly extend charging times. Traditional methods for determining charge rates often rely on rule-based approaches, which typically fail to effectively balance battery performance with charging duration. To address this, we introduce a novel optimization approach that directly integrates the dual objectives of minimizing charge time and maximizing battery lifetime into the optimization process. Unlike most existing charge optimization methods that do not directly track battery lifetime and charge time simultaneously, our method employs a data-driven model that facilitates direct and dynamic estimation of both battery lifetime and charge time at each step of the optimization process. Specifically, we utilize the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to predict battery capacity and voltage dynamics, which informs the calculations of lifetime and charge time required to solve the optimization problem. This approach provides a balanced optimization strategy that enhances the effectiveness of battery’s performance while maintaining the efficiency of the charging process. We applied this method to a novel next-generation NMC811 battery, featuring a cathode comprised of 80 % nickel, 10 % manganese, and 10 % cobalt, and a lithium metal foil anode - a combination not extensively studied previously. Experimental validation demonstrated that when optimized charge rates are applied every 10 cycles in a 100-cycle operation, the method leads to more stable cycling and improved capacity retention of approximately 7.4 % over the nominal charge rate, demonstrating the potential of the developed approach.

Poverty mapping in the age of machine learning

Recent years have witnessed considerable methodological advances in poverty mapping, much of which has focused on the application of modern machine-learning approaches to remotely-sensed data. Poverty maps produced with these methods generally share a common validation procedure, which assesses model performance by comparing sub-national poverty estimates with survey-based, direct estimates. While unbiased, direct estimates can be imprecise measures of true poverty rates, meaning that it is unclear whether these validation procedures are informative of actual model performance. In this paper, we use a rich dataset from Mexico to provide a more rigorous assessment of the modern approach to poverty mapping by evaluating its performance against a credible ground truth. We find that the modern method under-performs relative to benchmark traditional methods, largely because of the limited predictive capacity of remotely-sensed covariates. For a given covariate set, we also find that machine learning produces more biased poverty estimates than the traditional procedures, particularly for the poorest geographic areas.

The Discovery of Three Galactic Wolf–Rayet Stars

Wolf–Rayet stars (WRs) are evolved massive stars in the brief stage before they undergo core collapse. Not only are they rare, but they also can be particularly difficult to find due to the high extinction in the Galactic plane. This paper discusses the discovery of three new Galactic WRs previously classified as Hα emission stars, but thanks to Gaia spectra, we were able to identify the broad, strong emission lines that characterize WRs. Using the Lowell Discovery Telescope and the DeVeny spectrograph, we obtained spectra for each star. Two are WC9s, and the third is a WN6 + O6.5 V binary. The latter is a known eclipsing system with a 4.4 day period from ASAS-SN data. We calculate absolute visual magnitudes for all three stars to be between −7 and −6, which is consistent with our expectations of these subtypes. These discoveries highlight the incompleteness of the WR census in our local volume of the Milky Way and suggest the potential for future Galactic WR discoveries from Gaia low-dispersion spectra. Furthermore, radial velocity studies of the newly found binary will provide direct mass estimates and orbital parameters, adding to our knowledge of the role that binarity plays in massive star evolution.

Tipping the Scales: Indiscriminate Use of Interval Scales to Rate Diverse Dysarthric Features.

Error related to incorrect use of rating scales is problematic in the assessment and treatment of dysarthria. The main purpose of this project was to determine scale fit for cardinal speech features of hypokinetic dysarthria. A secondary aim was to determine rater reliability for the two different scales explored. Forty-three speakers with Parkinson's disease (PD) and 25 neurologically healthy control talkers were recorded reading sentences from the Speech Intelligibility Test. Twenty-two healthy female listeners used both an equal appearing interval (EAI) scale and a direct magnitude estimation (DME) scale to rate five perceptual speech features (i.e., overall speech severity, articulatory imprecision, reduced loudness, short rushes of speech, and monotony) from these recordings. Regression analyses were used to determine the linearity of the relationship between the means of the EAI and DME ratings. Inter- and intrarater reliability was calculated using intraclass correlation coefficients and Spearman's correlation coefficients, respectively, for both EAI and DME ratings. There was a linear relationship between EAI and DME means for monotony, indicating it is a metathetic dimension. Curvilinear relationships were observed between the EAI and DME means for the other four features, indicating prothetic dimensions. Intra- and interrater reliability values were similar for EAI and DME ratings. Overall, results of this work suggest that DME is the best fit for scaling several hypokinetic dysarthria features, and not the conventionally used EAI scale. Prothetic dimensions best scaled by DME include overall speech severity, articulatory imprecision, reduced loudness, and short rushes of speech. Monotony was the only feature found to be a metathetic dimension and would be best scaled using EAI or DME. Findings call for rethinking the widespread use of EAI scales for rating perceptual features as part of the assessment and treatment of motor speech disorders.

Prescribed‐time distributed direct estimation under relative state measurements

AbstractThe distributed estimation technology is prevalently utilized to solve the leader‐following multi‐agent tracking problem. This technology poses a challenge in practice, since it generally relies on the available absolute state measurements. For this reason, a novel distributed estimation approach based on relative state measurements is developed in this article. The proposed method directly estimates the tracking error between the leader and each follower, rather than using an existing indirect way of estimating and making subtraction under absolute state measurements. Specifically, a distributed directed estimation is first studied to complete estimation tasks within prescribed time under the known directed networks. Then, a fully distributed directed estimation problem is considered under the unknown directed networks. Both distributed and fully distributed results are extended to the robustness cases to resist external disturbances. Simulation examples, including numerical examples and a multiship coordination example, are provided to demonstrate the effectiveness and advantages of the proposed distributed estimation method.

Theoretical model of ultimate shear capacity and flexural capacity design method of boundary elements for reinforced concrete frames with steel plate shear walls

AbstractThe steel plate shear walls (SPSWs) have been proven effective in reinforced concrete frames (RCFs) as a lateral force‐resistant structure. Despite of advancements, accurately predicting the ultimate shear capacity of RCFs with SPSWs remains challenging using current simplified models. Additionally, the flexural capacity design procedure for the boundary elements (beams and columns) in previous studies of RCF‐SPSWs involved intricate iterative procedures, hindering its widespread implementation. To address the two issues, this paper investigates the pushover responses and the plate‐frame interaction (PFI) of an RCF‐SPSWs system using theoretical and numerical methods. There are three main contributions. First, a theoretical model of ultimate shear capacity for RCF‐SPSWs is proposed, which can also be used to predict shear contributions of boundary frames in RCF‐SPSWs. Calculation errors for ultimate shear capacity of RCF‐SPSWs and shear contribution from the boundary frame are only 3.7% and 6.7% respectively, which are reduced dramatically compared with the traditional model. A simplified schematic diagram for the global collapse mechanism (uniform distribution of plastic hinges within a structure) of RCF‐SPSWs is developed to facilitate the calculation of internal work and reaction forces. Secondly, a flexural capacity design method for the boundary elements to avoid in‐span plastic hinges is proposed. The proposed method enables the achievement of direct estimation of the flexural demands that could trigger a global collapse mechanism, all without intricate iterative procedures. The applicability of current assumptions for the design of steel boundary frame in RCF‐SPSWs system is discussed, and engineering suggestions are provided to ensure safer and more economic designs. Comparison results confirmed the applicability of the proposed design method, which can be adopted to achieve the global collapse mechanism for RCF‐SPSW system. Thirdly, impacts of yielding panel actions on the flexural capacity of boundary elements of RCF‐SPSWs are clarified. Comparison results demonstrated that adding SPSWs to an RCF alters the axial force on boundary elements and significantly impacts the flexural capacity. A design suggestion is made to emphasize the importance of avoiding the balanced failure of boundary elements. The proposed theoretical model can be used to economize the cross‐section of boundary elements in RCF‐SPSWs system under seismic loads due to accurate prediction of their shear contribution; the proposed flexural capacity design method can achieve a global collapse mechanism, and thus the structural safety and energy dissipation capacity are improved; moreover, the building design efficiency is also improved due to avoidance of intricate iterative procedures.

"Increasing Patient Loyalty: The Role Of Service Reliability, Brand Image, And Price With Integrity As The Key At The Executive Polyclinic Of Afiat PMI Bogor Hospital"

Business Aspects In the current era of globalization, hospital management is inevitable, this is also faced by PMI Bogor Hospital which is a class B private hospital located in the middle of Bogor City. Assessing the results of Google reviews until January 2024, PMI Hospital Bogor received a Rating rating of 3.7 out of 5.0, which means that the level of trust and patient satisfaction with PMI Bogor Hospital's services is still low. data on repeat visits of old patients at the Afiat polyclinic which reflects a cumulative decrease in patient loyalty, which is around 1-2% in 2023 compared to the previous year. The reliability of the service, brand image and price as well as integrity are suspected to be the causes. The research method used is quantitative descriptive with a cross sectional approach. The number of samples in this study was taken using a convenient sampling technique with a sample of 170 respondents. The data collection tool used used a questionnaire with a linkert scale. It was found that the increase in patient loyalty was indirectly positively influenced by brand image through service integrity as an intervention (indirect > direct estimation value; 0.284 > 0.171), and directly positively influenced by price (estimated value 0.663; P value 0.000) and Service Reliability (estimated value 0.170; P value 0.048) without going through integrity as an intervention. The conclusion of this study is that there is a significant positive influence between brand image variables that are influenced by integrity, but not on price and service reliability which directly have a positive effect on increasing patient loyalty without going through integrity variables

Small Area Estimation of Maluku and Papua Island Child Poverty Levels in 2023

Child welfare issues such as child poverty pose a challenge for Indonesia. The provinces in Maluku and Papua have the highest rates of child poverty. Data on child poverty at regencies/municipalities level is needed to address this issue through targeted policies. The direct estimations have a Relative Standard Error (RSE) value of more than 25 percent, necessitating the use of an indirect method, Small Area Estimation (SAE). This study aims to compare the results of indirect estimates of the percentage of children aged 0-17 living in poverty at the regencies/municipalities level in Maluku and Papua using SAE Empirical Best Linear Unbiased Prediction (EBLUP) and Hierarchical Bayes Beta (HB Beta) methods. Susenas KOR March 2023 data is used to produce direct estimates, while Podes 2021 data is used to form auxiliary variables. The results indicate that the SAE HB Beta method provides estimates with better RSE compared to SAE EBLUP. All regencies/municipalities in the Maluku and Papua have a fairly good level of accuracy.

Biomass burning CO emissions: exploring insights through TROPOMI-derived emissions and emission coefficients

Abstract. Emissions from biomass burning are a significant source of air pollution, which can adversely impact air quality and ecosystems thousands of kilometres downwind. These emissions can be estimated by a bottom-up approach that relies on fuel consumed and standardized emission factors. Emissions are also commonly derived with a top-down approach, using satellite-observed fire radiative power (FRP) as a proxy for fuel consumption. Biomass burning emissions can also be estimated directly from satellite trace gas observations, including carbon monoxide (CO). Here, we explore the potential of satellite-derived CO emission rates from biomass burning and provide new insights into the understanding of satellite-derived fire CO emissions globally, with respect to differences in regions and vegetation type. Specifically, we use the TROPOMI (Tropospheric Monitoring Instrument) high-spatial-resolution satellite datasets to derive burning CO emissions directly for individual fires between 2019 and 2021 globally. Using synthetic data (with known emissions), we show that the direct emission estimate methodology has a 34 % uncertainty for deriving CO emissions (and a total uncertainty of 44 % including wind and CO column uncertainty). From the TROPOMI-derived CO emissions, we derive biome-specific emission coefficients (emissions relative to FRP) by combining the direct emission estimates and the satellite-observed FRP from the Moderate Resolution Imaging Spectrometer (MODIS). These emission coefficients are used to establish annual top-down CO emission inventories from biomass burning, showing that Southern Hemisphere Africa has the highest CO biomass burning emissions (over 25 % of global total of 300–390 Mt(CO) yr−1 between 2003–2021), and almost 25 % of global CO biomass burning emissions are from broadleaved evergreen tree fires. A comprehensive comparison between direct estimates, top-down and bottom-up approaches, provides insight into the strengths and weaknesses of each method: FINN2.5 has higher CO emissions, by a factor between 2 and 5, than all other inventories assessed in this study. Trends over the past 2 decades are examined for different regions around the globe, showing that global CO biomass burning emissions have, on the whole, decreased (by 5.1 to 8.7 Mt(CO) yr−1), where some regions experience increased and others decreased emissions.

Simultaneous estimation of multiple order phase derivatives using deep learning method in digital holographic interferometry

For non-contact deformation testing, digital holographic interferometry is a prominent optical technique where the first and second order interference phase derivatives directly embed information about the strain and curvature distributions of a deformed object. Hence, reliable extraction of multiple order phase derivatives is of great practical significance; however, this problem is marred by several challenges such as the need of multiple differentiation operations, complex shearing operations and performance degradation due to noise. In this paper, we introduce a deep learning approach for the direct and simultaneous estimation of first and second order phase derivatives in digital holographic interferometry. Our method's performance is demonstrated via rigorous numerical simulations exhibiting wide range of additive white Gaussian noise and speckle noise. Moreover, we substantiate the practical efficacy of our proposed method for processing deformation fringes acquired via digital holographic interferometry.

465 Comparative analysis of semantic segmentation and deep regression models with supervised pre-training for accurate prediction of pig body weight from video data: Insights from industry-scale datasets

Abstract Accurate pig body weight (BW) measurement is essential for producers as it is related to pig growth, health, and marketing, yet conventional manual weighing methods are time-consuming and may cause potential stress to the animals. Although there is a growing trend towards the adoption of three-dimensional cameras coupled with computer vision techniques for pig BW estimation, their validation using industry-scale data are still limited. Among the prevailing methodologies, semantic segmentation and supervised pre-training regression are prominent models currently used. Therefore, the objectives of this study were: 1) to estimate pig BW from repeatedly measured video data obtained from a commercial setting, 2) to compare the performance of the two image analysis methods: thresholding segmentation and deep regression approaches, and 3) to evaluate the predictive ability of BW estimation models. An Intel RealSense D435 camera was installed in the commercial farm to collect top-view videos of 540 pigs biweekly at five different time points over three months. At the same time, manually measured BW records were collected using a digital weighing system. We used an automated video conversion pipeline and fine-tuned YOLOv8 to pre-process the raw depth videos. Subsequently, we acquired a total of 151,756 depth images and depth map files. Adaptive thresholding was applied to segment the pig body from the background. Four image-derived biometric features, including dorsal length, abdominal width, height, and volume, were estimated from the segmented images and fitted using ordinary least squares and random forest models. We applied transfer learning by initializing the weights of five deep learning models, including ResNet50, Xception, EfficientNetV2S, ConvNeXtBase, and Vision Transformer, with pre-trained weights from ImageNet. We then fine-tuned these models on the pig depth images. The last layer of each model was adapted to linear regression, enabling direct estimation of BW for each image without the need for additional image pre-processing steps. We employed random repeated subsampling cross-validation, dividing 80% of the pigs for training and 20% for testing, to evaluate prediction performance at each time point. The best prediction coefficients of determination and mean absolute percentage error for each time point were 0.76, 0.86, 0.90, 0.83, 0.90, and 4.57%, 3.76%, 3.01%, 3.41%, 4.84%, respectively. On average, the Xception model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.90 and 3.01%. Our results suggest that deep learning-based supervised learning models improve the prediction performance of pig BW from industry-scale depth video data.

On indirect estimation of small area parameters under ranked set sampling

In investigations of domains under post-stratified random sampling, it is difficult to get an acceptable precision for domain-specific estimates due to low sample sizes. Small area estimate, a popular technique that has been widely used over the past few decades, involves indirect estimating using the auxiliary data from the entire population. In this article, we utilize a ranked set sampling (RSS) technique to achieve a greater level of precision in area-specific estimations under the assumption that ranking the smaller sets is simple, inexpensive, and flawless. RSS optimizes sample size for a fixed degree of precision or increases precision for a fixed sample size. We create direct estimators for population total under homogeneous, ratio, and regression models that are area specific. To evaluate the effectiveness and application of the suggested RSS technique, data from the Pakistan Demographic Health Survey (PDHS 2017–18) and Iris flower data are used. The effectiveness of the RSS mechanism is supported by both theoretical characteristics and Bootstrapped tests.

Developing a framework for identifying risk factors and estimating direct economic disease burden attributable to healthcare-associated infections: a case study of a Chinese Tuberculosis hospital

Healthcare-associated infections (HAIs) represent a major global health burden, which necessitate effective frameworks to identify potential risk factors and estimate the corresponding direct economic disease burden. In this article, we proposed a framework designed to address these needs through a case study conducted in a Tuberculosis (TB) hospital in Hubei Province, China, using data from 2018 to 2019. A comprehensive multistep procedure was developed, including ethical application, participant inclusion, risk factor identification, and direct economic disease burden estimation. In the case study, ethical approval was obtained, and patient data were anonymized to ensure privacy. All TB hospitalized patients over the study period were included and classified into groups with and without HAIs after screening the inclusion and exclusion criteria. Key risk factors, including gender, age, and invasive procedure were identified through univariate and multivariate analyses. Then, propensity score matching was employed to select the balanced groups with similar characteristics. Comparisons of medical expenditures (total medical expenditure, medicine expenditure, and antibiotics expenditure) and hospitalization days between the balanced groups were calculated as the additional direct economic disease burden measures caused by HAIs. This framework can serve as a tool for not only hospital management and policy-making, but also implementation of targeted infection prevention and control measures. Moreover, it has the potential to be applied in various healthcare settings at local, regional, national, and international levels to identify high-risk areas, optimize resource allocation, and improve hospital management and governance, as well as inter-organizational learning. Challenges to implement the framework are also raised, such as data quality, regulatory compliance, considerations on unique nature of communicable diseases and other diseases, and training need for professionals.

Differences between sexes in STEMI treatment and outcomes with contemporary primary PCI.

Historically, differences in timely reperfusion and outcomes have been described in females who suffer ST-segment elevation myocardial infarction (STEMI). However, there have been improvements in the treatment of STEMI patients with contemporary Percutaneous Coronary Intervention (PCI) strategies. Comparisons between sexes were performed on STEMI patients treated with primary PCI over a 4-year period (January 1, 2017-December 31, 2020) from the Queensland Cardiac Outcomes Registry. Primary outcomes were 30-day and 1-year cardiovascular mortality. Secondary outcomes were STEMI performance measures. The total and direct effects of gender on mortality outcomes were estimated using logistic and multinomial logistic regression models. Overall, 2747 (76% male) were included. Females were on average older (65.9 vs. 61.9 years; p < 0.001), had longer total ischemic time (69 min vs. 52 min; p < 0.001) and less achievement of STEMI performance targets (<90 min) (50% vs. 58%; p < 0.001). There was no evidence for a total (odds ratio [OR] 1.3 (95% confidence interval [CI]: 0.8-2.2; p = 0.35) or direct (adjusted OR 1.2 (95% CI: 0.7-2.1; p = 0.58) effect of female sex on 30-day mortality. One-year mortality was higher in females (6.9% vs. 4.4%; p = 0.014) with total effect estimates consistent with increased risk of cardiovascular mortality (Incidence rate ratio [IRR]: 1.5; 95% CI: 1.0-2.3; p = 0.059) and noncardiovascular mortality (IRR: 2.1; 95% CI: 0.9-4.7; p = 0.077) in females. However, direct (adjusted) effect estimates of cardiovascular mortality (IRR: 1.0; 95% CI: 0.6-1.6; p = 0.94) indicated sex differences were explained by confounders and mediators. Small sex differences in STEMI performance measures still exist; however, with contemporary primary PCI strategies, sex is not associated with cardiovascular mortality at 30 days or 1 year.

Intergenerational Chain of Violence, Adverse Childhood Experiences, and Elder Abuse Perpetration

It is widely known that individuals with adverse childhood experiences (ACEs) have an increased risk of abusing their own children, thereby perpetuating the cycle of violence. However, the association between ACEs and elder abuse perpetration has not been fully examined. To examine the association between ACEs and elder abuse and the mediating factors. This cross-sectional study used data collected via the self-administered Japan COVID-19 and Society Internet Survey from September 12 to October 19, 2022. Men and women aged 20 to 64 years who responded to related questions were included. Data were analyzed from July 2023 to April 2024. ACEs, defined as the experience of any of 7 items-interpersonal loss (parental loss and parental divorce), family psychopathology (parental mental disease and violence in family), abuse (physical and psychological abuse), and neglect-before the age of 18 years. The primary outcome was the perpetration of physical and/or psychological abuse against an older person (aged ≥65 years) self-reported via questionnaire. The direct and indirect effect estimates were determined using logistic regression analyses. Of a total of 13 318 participants (mean [SD] age, 41.1 [12.1] years; 6634 female [49.8%]), 1133 (8.5%) reported perpetrating violence against older adults. Compared with individuals without ACEs, the odds ratios (ORs) for perpetrating violence were 3.22 (95% CI, 2.74-3.79) for those with 1 ACE and 7.65 (95% CI, 6.41-9.13) for those with 2 or more ACEs. In the mediation analysis, factors with large indirect effect estimates included depression (OR, 1.13; 95% CI, 1.11-1.14; proportion mediated [PM], 18.6%), mental illness other than depression (OR, 1.12; 95% CI, 1.10-1.14; PM, 17.3%), and self-rated health (OR, 1.04; 95% CI, 1.03-1.05; PM, 6.0%). These findings suggest that intergenerational cycles of violence may extend to any vulnerable group, not only children but also older adults. Further research into the prevention of ACEs and breaking these cycles of violence is warranted.

Automated Analysis of Stereotypical Movements in Videos of Children With Autism Spectrum Disorder

Stereotypical motor movements (SMMs) are a form of restricted and repetitive behavior, which is a core symptom of autism spectrum disorder (ASD). Current quantification of SMM severity is extremely limited, with studies relying on coarse and subjective caregiver reports or laborious manual annotation of short video recordings. To assess the utility of a new open-source AI algorithm that can analyze extensive video recordings of children and automatically identify segments with heterogeneous SMMs, thereby enabling their direct and objective quantification. This retrospective cohort study included 241 children (aged 1.4 to 8.0 years) with ASD. Video recordings of 319 behavioral assessments carried out at the Azrieli National Centre for Autism and Neurodevelopment Research in Israel between 2017 and 2021 were extracted. Behavioral assessments included cognitive, language, and autism diagnostic observation schedule, 2nd edition (ADOS-2) assessments. Data were analyzed from October 2020 to May 2024. Each assessment was recorded with 2 to 4 cameras, yielding 580 hours of video footage. Within these extensive video recordings, manual annotators identified 7352 video segments containing heterogeneous SMMs performed by different children (21.14 hours of video). A pose estimation algorithm was used to extract skeletal representations of all individuals in each video frame and was trained an object detection algorithm to identify the child in each video. The skeletal representation of the child was then used to train an SMM recognition algorithm using a 3 dimensional convolutional neural network. Data from 220 children were used for training and data from the remaining 21 children were used for testing. Among 319 behavioral assessment recordings from 241 children (172 [78%] male; mean [SD] age, 3.97 [1.30] years), the algorithm accurately detected 92.53% (95% CI, 81.09%-95.10%) of manually annotated SMMs in our test data with 66.82% (95% CI, 55.28%-72.05%) precision. Overall number and duration of algorithm-identified SMMs per child were highly correlated with manually annotated number and duration of SMMs (r = 0.8; 95% CI, 0.67-0.93; P < .001; and r = 0.88; 95% CI, 0.74-0.96; P < .001, respectively). This study suggests the ability of an algorithm to identify a highly diverse range of SMMs and quantify them with high accuracy, enabling objective and direct estimation of SMM severity in individual children with ASD.

Cataloging health state utility estimates for Duchenne muscular dystrophy and related conditions

BackgroundDuchenne muscular dystrophy (DMD) is a genetic disease resulting in progressive muscle weakness, loss of ambulation, and cardiorespiratory complications. Direct estimation of health-related quality of life for patients with DMD is challenging, highlighting the need for proxy measures. This study aims to catalog and compare existing published health state utility estimates for DMD and related conditions.MethodsUsing two search strategies, relevant utilities were extracted from the Tufts Cost-Effectiveness Analysis Registry, including health states, utility estimates, and study and patient characteristics. Analysis One identified health states with comparable utility estimates to a set of published US patient population utility estimates for DMD. A minimal clinically important difference of ± 0.03 was applied to each DMD utility estimate to establish a range, and the registry was searched to identify other health states with associated utilities that fell within each range. Analysis Two used pre-defined search terms to identify health states clinically similar to DMD. Mapping was based on the degree of clinical similarity.ResultsAnalysis One identified 4,308 unique utilities across 2,322 cost-effectiveness publications. The health states captured a wide range of acute and chronic conditions; 34% of utility records were extrapolated for US populations (n = 1,451); 1% were related to pediatric populations (n = 61). Analysis Two identified 153 utilities with health states clinically similar to DMD. The median utility estimates varied among identified health states. Health states similar to the early non-ambulatory DMD phase exhibited the greatest difference between the median estimate of the sample (0.39) and the existing estimate from published literature (0.21).ConclusionsWhen available estimates are limited, using novel search strategies to identify utilities of clinically similar conditions could be an approach for overcoming the information gap. However, it requires careful evaluation of the utility instruments, tariffs, and raters (proxy or self).

Comparison of ten formulae for calculating low-density lipoprotein cholesterol with direct low-density lipoprotein cholesterol measurement

ABSTRACT Background: Accurate lipid profile assessment, mainly low-density lipoprotein cholesterol (LDL-C) measurement, is crucial for diagnosing and managing cardiovascular disease (CVD). The widely used Friedewald formula for LDL-C calculation has limitations, prompting the exploration of alternative equations. Aims: This study aims to compare ten LDL-C calculation formulas with direct measurement among study participants attending a tertiary care centre. Materials and Methods: Conducted from September 2021 to July 2022 at a tertiary healthcare centre in India, the study involved 114 participants aged 18 years or older. The exclusion criteria were high-serum triglyceride (TG) levels, renal failure, diabetes, liver diseases or unwillingness to participate. Blood samples were analysed for total cholesterol, TG, high-density lipoprotein cholesterol and LDL-C. The very-low-density lipoprotein cholesterol level was calculated. The LDL-C formulas evaluated were Friedewald, Anandaraja, De Cordova, Vujovic, Ahmadi, Puavillai, Chen, Hattori, Martin–Hopkin and Sampson. Results: Vujovic’s formula showed a negligible mean difference and strong correlation (r = 0.916, P &lt; 0.001) with direct LDL-C estimation across TG concentrations. Sampson’s formula performed best for TG &lt;150 mg/dL, whereas Vujovic’s formula excelled for 151–399 mg/dL TG concentrations. All formulas were highly correlated with direct LDL-C levels (P &lt; 0.001). Bland–Altman analysis indicated Vujovic’s formula as the closest match to direct measurement, with a mean bias of −1.42 ± 20.59 mg/dL. Conclusion: Vujovic’s formula demonstrated the slightest difference and strong correlation with direct LDL-C measurement, outperforming other formulas. This finding enhances the accuracy of LDL-C assessment and improves CVD risk management in clinical practice.

A decoupling estimation method for baseline load of photovoltaic user clusters based on multidimensional abnormal data restoration

Abstract With the widespread installation of distributed photovoltaics, their fluctuation characteristics with weather changes have posed significant challenges to baseline load estimation. In addition, due to measurement and collection anomalies, it is easy to cause continuous missing and abnormal data collection. To address the issue of low baseline estimation accuracy caused by distributed photovoltaic fluctuation characteristics and abnormal data collection, this paper proposes a photovoltaic user cluster baseline load decoupling estimation method based on multi-dimensional abnormal data repair. Firstly, a multidimensional anomaly data repair system is established based on extreme learning machines and multi-step prediction. Secondly, using the repaired data for baseline estimation, photovoltaic users and non-photovoltaic users are decoupled and separated. Long short-term memory neural network estimation models are used for non-photovoltaic users, and baseline load estimation methods based on weather type analysis are used for photovoltaic users. Finally, the baseline results are accumulated. The simulation results show that compared with the direct estimation method of baseline load, the accuracy of decoupling estimation has improved by 33.7%.