External Validation Process Research Articles

Creating a content validation process to advance a campus-wide leadership framework

PurposeA content validation process of an institutional leadership framework is described for leadership educators in higher education. We created this process to further integrate our leadership framework across campus, maintain alignment with advancements in leadership research and ensure it is broadly inclusive and culturally responsive.Design/methodology/approachOur approach included seven essential design elements and was informed by a review of leadership frameworks in practice and the literature, validation studies and a comprehensive document review.FindingsOur approach yielded a validated leadership framework with modifications to its principles, values, competencies and outcomes. Modifications addressed pre-determined criteria and were deemed relevant to leadership research and our institutional context.Originality/valueThe external content validation process of our leadership framework is novel and serves as a valuable guide for those considering opportunities to strengthen their own institutional approaches to leadership education.

Enhancing clinical decision-making: A novel nomogram for stratifying cancer-specific survival in middle-aged individuals with follicular thyroid carcinoma utilizing SEER data

BackgroundThyroid cancer (TC) is the most common malignant tumor in the endocrine system, is also one of the head and neck tumor. Follicular Thyroid Carcinoma (FTC) plays an important role in the pathological classification of thyroid cancer. This study aimed to develop an innovative predictive tool, a nomogram, for predicting cancer specific survival (CSS) in middle-aged FTC patients. MethodsWe collected patient data from the Surveillance, Epidemiology, and End Results (SEER) database. The data from patients between 2004 and 2015 were used as the training set, and the data from patients between 2016 and 2018 were used as the validation set. To identify independent risk factors affecting patient survival, univariate and multivariate Cox regression analyses were performed. Based on this, we developed a nomogram model aimed at predicting CSS in middle-aged patients with FTC. The consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration curve were used to evaluate the accuracy and confidence of the model. ResultsA total of 2470 patients were enrolled in this study, in which patients from 2004 to 2015 were randomly assigned to the training cohort (N = 1437) and validation cohort (N = 598), and patients from 2016 to 2018 were assigned to the external validation cohort (N = 435) in terms of time. Univariate and multivariate Cox regression analysis showed that marriage, histological grade and TNM stage were independent risk factors for survival. The C-index for the training cohort was 0.866 (95 % CI: 0.805–0.927), for the validation cohort it was 0.944 (95 % CI: 0.903–0.985), and for the external validation cohort, it reached 0.999 (95 % CI: 0.997–1.001). Calibration curves and AUC suggest that the model has good accuracy. ConclusionsWe developed an innovative nomogram to predict CSS in middle-aged patients with FTC. Our model after a rigorous internal validation and external validation process, based on the time proved that the high level of accuracy and reliability. This tool helps healthcare professionals and patients make informed clinical decisions.

Effectiveness of near-infrared spectroscopy to predict the chemical composition of feces and total-tract apparent nutrients digestibility estimated with undigestible neutral detergent fiber or acid-insoluble ash in lactating buffaloes' feces

Following a comparison of nutrient total-tract digestibility estimates in lactating buffaloes using single-point undigestible NDF (uNDF) or acid-insoluble ash (AIA) as internal markers, the potential of fecal near-infrared spectroscopy (NIRS) to provide calibration equations for the assessment of the chemical composition of feces and nutrient total-tract digestibility estimated with internal markers was explored. Chemical analyses were performed on 147 fecal samples from lactating buffaloes reared on 5 farms in central Italy (Naples). Each farm fed a silage-based TMR to the buffaloes, and the TMR was sampled in the 2 d before the fecal collection. The TMR and individual fecal samples were collected and analyzed for DM, OM, ash, AIA, ether extract (EE), starch, fiber fractions (amylase-treated NDF without residual ash [aNDFom], amylase-treated NDF inclusive of residual ash [aNDF], ADF without residual ash [ADFom], ADF, hemicellulose, cellulose, ADL, uNDF), N, CP and CP bound to aNDF (NDICP) and to ADF (ADICP). The uNDF content was determined through a 240-h in vitro fermentation and employed, together with AIA as markers, to estimate the total-tract apparent digestibility and total-tract digestibility of DM, OM, ash, N, CP, EE, aNDFom, aNDF, NDIP, ADFom, and ADF, ADIN, ADL, hemicellulose, cellulose, starch, NFC, and the B3 fraction of N (NB3). No correlation was found between DM and OM digestibility estimated with AIA and uNDF as internal markers. Weak correlations were detected for all the other nutients digestibilities, and strong correlations were observed for EE, ADFom, hemicellulose, NDIN, ADIN, NB3, NFC, and starch. The sample set (n = 147) was divided in a calibration set (n = 111) and a validation set (n = 36) to "train" and "validate" the fecal NIRS curve through an external validation process. An estimation usable for preliminary or initial evaluation was obtained for N, CP, and aNDF fecal content. An excellent prediction was obtained for total tract digestibility of ADIN (R2 = 0.90) when estimated with uNDF as the internal marker. The NIRS technology was not able to accurately predict all the other traits and the estimated nutrient digestibility of lactating buffalo diets from fecal spectra.

Prediction of Pseudomonas spp. Population in Food Products and Culture Media Using Machine Learning-Based Regression Methods.

Machine learning approaches are alternative modelling techniques to traditional modelling equations used in predictive food microbiology and utilise algorithms to analyse large datasets that contain information about microbial growth or survival in various food matrices. These approaches leverage the power of algorithms to extract insights from the data and make predictions regarding the behaviour of microorganisms in different food environments. The objective of this study was to apply various machine learning-based regression methods, including support vector regression (SVR), Gaussian process regression (GPR), decision tree regression (DTR), and random forest regression (RFR), to estimate bacterial populations. In order to achieve this, a total of 5618 data points for Pseudomonas spp. present in food products (beef, pork, and poultry) and culture media were gathered from the ComBase database. The machine learning algorithms were applied to predict the growth or survival behaviour of Pseudomonas spp. in food products and culture media by considering predictor variables such as temperature, salt concentration, water activity, and acidity. The suitability of the algorithms was assessed using statistical measures such as coefficient of determination (R2), root mean square error (RMSE), bias factor (Bf), and accuracy (Af). Each of the regression algorithms showed appropriate estimation capabilities with R2 ranging from 0.886 to 0.913, RMSE from 0.724 to 0.899, Bf from 1.012 to 1.020, and Af from 1.086 to 1.101 for each food product and culture medium. Since the predictive capability of RFR was the best among the algorithms, externally collected data from the literature were used for RFR. The external validation process showed statistical indices of Bf ranging from 0.951 to 1.040 and Af ranging from 1.091 to 1.130, indicating that RFR can be used for predicting the survival and growth of microorganisms in food products. Therefore, machine learning approaches can be considered as an alternative to conventional modelling methods in predictive microbiology. However, it is important to highlight that the prediction power of the machine learning regression method directly depends on the dataset size, and it requires a large dataset to be employed for modelling. Therefore, the modelling work of this study can only be used for the prediction of Pseudomonas spp. in specific food products (beef, pork, and poultry) and culture medium with certain conditions where a large dataset is available.

Clinical practice competencies for standard critical care nursing: consensus statement based on a systematic review and Delphi survey

ObjectivesA clear development process and scientifically validated clinical practice competencies in standard critical care nursing (SCCN) have not yet been developed in Japan. Thus, this study aimed to develop a...

Urine Cellular DNA Point Mutation and Methylation for Identifying Upper Tract Urinary Carcinoma.

Simple SummaryIt’s difficult to detect upper tract urothelial carcinoma at early stage. Invasive testing may increase risk of cancer recurrences in the bladder after radical nephroureterectomy. Thus, in the present study, we incorporated two-gene mutation and methylation biomarkers to conduct the diagnostic tool of upper tract urothelial carcinoma and performed external validation to investigate the utility and stability of the optimal panel. It showed a highly specific and robust performance. It may be used as a replaceable approach for early detection of upper tract urothelial carcinoma, resulting in less extensive examinations in patients at low risk. Background: To improve the selection of patients for ureteroscopy, avoid excessive testing and reduce costs, we aimed to develop and validate a diagnostic urine assay for upper tract urinary carcinoma (UTUC). Methods: In this cohort study we recruited 402 patients from six Hunan hospitals who underwent ureteroscopy for hematuria, including 95 patients with UTUC and 307 patients with non-UTUC findings. Midstream morning urine samples were collected before ureteroscopy and surgery. DNA was extracted and qPCR was used to analyze mutations in TERT and FGFR3 and the methylation of NRN1. In the training set, the random forest algorithm was used to build an optimal panel. Lastly, the Beijing cohort (n = 76) was used to validate the panel. Results: The panel combining the methylation with mutation markers led to an AUC of 0.958 (95% CI: 0.933–0.975) with a sensitivity of 91.58% and a specificity of 94.79%. The panel presented a favorable diagnostic value for UTUC vs. other malignant tumors (AUC = 0.920) and UTUC vs. benign disease (AUC = 0.975). Furthermore, combining the panel with age revealed satisfactory results, with 93.68% sensitivity, 94.44% specificity, AUC = 0.970 and NPV = 98.6%. In the external validation process, the model showed an AUC of 0.971, a sensitivity of 95.83% and a specificity of 92.31, respectively. Conclusions: A novel diagnostic model for analyzing hematuria patients for the risk of UTUC was developed, which could lead to a reduction in the need for invasive examinations. Combining NRN1 methylation and gene mutation (FGFR3 and TERT) with age resulted in a validated accurate prediction model.

Near-Infrared Spectroscopy (NIRS) as a Method for Biological Sex Discrimination in the Endangered Houston Toad (Anaxyrus houstonensis).

Biological sex is one of the more critically important physiological parameters needed for managing threatened animal species because it is crucial for informing several of the management decisions surrounding conservation breeding programs. Near-infrared spectroscopy (NIRS) is a non-invasive technology that has been recently applied in the field of wildlife science to evaluate various aspects of animal physiology and may have potential as an in vivo technique for determining biological sex in live amphibian species. This study investigated whether NIRS could be used as a rapid and non-invasive method for discriminating biological sex in the endangered Houston toad (Anaxyrus houstonensis). NIR spectra (N = 396) were collected from live A. houstonensis individuals (N = 132), and distinct spectral patterns between males and females were identified using chemometrics. Linear discriminant analysis (PCA-LDA) classified the spectra from each biological sex with accuracy ≥ 98% in the calibration and internal validation datasets and 94% in the external validation process. Through the use of NIRS, we have determined that unique spectral signatures can be holistically captured in the skin of male and female anurans, bringing to light the possibility of further application of this technique for juveniles and sexually monomorphic species, whose sex designation is important for breeding-related decisions.

Validation of a novel risk score to predict mortality after surgery for acute type A dissection.

The aim of this study was to externally validate a lab-based risk score (lactate, creatinine, aspartate aminotransferase, alanine aminotransferase or bilirubin) by Ghoreishi et al. to predict perioperative mortality in patients undergoing surgical repair for acute type A aortic dissection. The risk score to predict operative mortality was applied to a large and homogenous validation cohort that consisted of 632 patients undergoing surgery for acute type A aortic dissection in 2 centres. Multivariable regression analysis was performed to determine the impact on survival. Receiver operating characteristics with deduced area under the curve were used to assess the ability to predict perioperative mortality. A total of 632 patients (54% male, mean age 62 ± 14 years) were assigned to 3 different risk groups according to the calculated mortality score [low risk <7 (31.2%), moderate risk 7-20 (36.1%) and high >20 (32.7%)]. Perioperative mortality was 8% in the low-risk group, 10% in the moderate-risk group and 24% in the high-risk group (P < 0.0001). Receiver operating characteristic analysis of this new score revealed an area under the curve of 0.69 with adequate calibration. In addition, multivariable analysis revealed an independet assocation with perioperative mortality (odds ratio 1.509; 95% confidence interval 1.042-2.185). While overall survival differed between the risk groups (P < 0.0001), the score does not serve as an independent predictor of long-term mortality when adjusted for relevant covariates. The external validation process confirmed that a newly proposed risk score offers clinicians a helpful and reliable tool to improve the preoperative risk assessment of acute type A aortic dissection patients based on easily accessible and broadly available laboratory parameters.

The development and validation of prognostic models for overall survival in the presence of missing data in the training dataset: a strategy with a detailed example

BackgroundThe United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology.MethodsModel buildingThe training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin’s rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin’s rules to define the prognostic model.Model assessmentCalibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin’s rules.Model validationThe D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified.Development of risk groupsRisk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin’s rules.ResultsThe training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716–0.964) in the training dataset and 0.654 (95% CI 0.497–0.811) in the test dataset and the corrected D-Statistic was 0.801.ConclusionThe decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model.Trial registrationBoth trials were registered; Myeloma IX-ISRCTN68454111, registered 21 September 2000. Myeloma XI-ISRCTN49407852, registered 24 June 2009.

The Current Landscape of College-Sponsored Postgraduate Teaching and Learning Curriculum Programs

Objective. To describe the landscape of teaching and learning curriculum (TLC) programs sponsored by US schools and colleges of pharmacy and evaluate their adoption of best practice recommendations. Methods. A 28-item electronic survey instrument was developed based on best practice recommendations published by the American Association of Colleges of Pharmacy (AACP), American Society of Health-System Pharmacists (ASHP), and American College of Clinical Pharmacy (ACCP) for the conduct of TLC programs. The survey instrument was electronically distributed to 137 accredited colleges and schools of pharmacy in the United States. Results. Eighty-eight institutions responded, resulting in a response rate of 64%. Sixty-one TLC programs were included in the final analysis. Seventy-five percent of TLC programs reported using best practice recommendations; however, 10% of respondents indicated they were not aware of the published recommendations. Inconsistencies among programs were noted in required teaching experiences, participant evaluation, and ongoing programmatic assessment. Conclusion. Most institutions offering TLC programs are aware of published best practice guidelines and have adopted a majority of the published best practices. However, considerable variability exists across the country. Development of a formal external validation process for TLC programs is necessary to ensure consistent quality.

Deep diving into values: A way to enhance empowerment evaluation

In recent years, the New South Wales government education system changed the way whole-school evaluation occurs. Moving away from external school reviews when data suggested underperformance, principals are now required to develop 3-year strategic school plans and self-evaluate them in consultation with their staff, parents and students. An external validation process is then undertaken by principal peers. The internal school process presumes a stakeholder-engagement approach to school planning and evaluation. It further presumes that stakeholders are not only consulted but also feel they understand and own the plan. One school principal, realising the challenges that the new model posed for himself and his staff, engaged an evaluation team to develop and implement a process that would help his school rise to these challenges. This article describes the empowerment evaluation process that ensued. It first explains the context of the school that gave rise to empowerment over other forms of stakeholder-engagement evaluation processes. It discusses how the literature on values underpinned the conceptual framework and operational model. The article then illustrates how the process enabled the staff to engage explicitly with personal and organisational values and how a focus on these values was built into every stage of the process. Finally, the benefits as well as the challenges of this approach are described.

Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event.

Introduction: Recurrent stroke has a higher rate of death and disability. A number of risk scores have been developed to predict short-term and long-term risk of stroke following an initial episode of stroke or transient ischemic attack (TIA) with limited clinical utilities. In this paper, we review different risk score models and discuss their validity and clinical utilities.Methods: The PubMed bibliographic database was searched for original research articles on the various risk scores for risk of stroke following an initial episode of stroke or TIA. The validation of the models was evaluated by examining the internal and external validation process as well as statistical methodology, the study power, as well as the accuracy and metrics such as sensitivity and specificity.Results: Different risk score models have been derived from different study populations. Validation studies for these risk scores have produced conflicting results. Currently, ABCD2 score with diffusion weighted imaging (DWI) and Recurrence Risk Estimator at 90 days (RRE-90) are the two acceptable models for short-term risk prediction whereas Essen Stroke Risk Score (ESRS) and Stroke Prognosis Instrument-II (SPI-II) can be useful for prediction of long-term risk.Conclusion: The clinical risk scores that currently exist for predicting short-term and long-term risk of recurrent cerebral ischemia are limited in their performance and clinical utilities. There is a need for a better predictive tool which can overcome the limitations of current predictive models. Application of machine learning methods in combination with electronic health records may provide platform for development of new-generation predictive tools.

Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms.

Uterine cancer (also known as endometrial cancer) can seriously affect the female reproductive system, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. Due to the limited ability to model the complicated relationships between histopathological images and their interpretations, existing computer-aided diagnosis (CAD) approaches using traditional machine learning algorithms often failed to achieve satisfying results. In this study, we develop a CAD approach based on a convolutional neural network (CNN) and attention mechanisms, called HIENet. In the ten-fold cross-validation on ∼3,300 hematoxylin and eosin (H&E) image patches from ∼500 endometrial specimens, HIENet achieved a 76.91 ± 1.17% (mean ± s. d.) accuracy for four classes of endometrial tissue, i.e., normal endometrium, endometrial polyp, endometrial hyperplasia, and endometrial adenocarcinoma. Also, HIENet obtained an area-under-the-curve (AUC) of 0.9579 ± 0.0103 with an 81.04 ± 3.87% sensitivity and 94.78 ± 0.87% specificity in a binary classification task that detected endometrioid adenocarcinoma. Besides, in the external validation on 200 H&E image patches from 50 randomly-selected female patients, HIENet achieved an 84.50% accuracy in the four-class classification task, as well as an AUC of 0.9829 with a 77.97% (95% confidence interval, CI, 65.27%∼87.71%) sensitivity and 100% (95% CI, 97.42%∼100.00%) specificity. The proposed CAD method outperformed three human experts and five CNN-based classifiers regarding overall classification performance. It was also able to provide pathologists better interpretability of diagnoses by highlighting the histopathological correlations of local pixel-level image features to morphological characteristics of endometrial tissue.

Shortwave Near-Infrared Spectroscopy for Rapid Detection of Aflatoxin B1 Contamination in Polished Rice

The objective of this research was to apply near-infrared spectroscopy, with a short-wavelength range of 950 to 1,650 nm, for the rapid detection of aflatoxin B1 (AFB1) contamination in polished rice samples. Spectra were obtained by reflection mode for 105 rice samples: 90 samples naturally contaminated with AFB1 and 15 samples artificially contaminated with AFB1. Quantitative calibration models to detect AFB1 were developed using the original and pretreated absorbance spectra in conjunction with partial least squares regression with prediction testing and full cross-validation. The statistical model from the external validation process developed from the treated spectra (standard normal variate and detrending) was most accurate for prediction, with a correlation coefficient (r) of 0.952, a standard error of prediction of 3.362 μg/kg, and a bias of -0.778 μg/kg. The most predictive models according to full cross-validation were developed from the multiplicative scatter correction pretreated spectra (r = 0.967, root mean square error in cross-validation [RMSECV] = 2.689 μg/kg, bias = 0.015 μg/kg) and standard normal variate pretreated spectra (r = 0.966, RMSECV = 2.691 μg/kg, bias = 0.008 μg/kg). A classification-based partial least squares discriminant analysis model of AFB1 contamination classified the samples with 90% accuracy. The results indicate that the near-infrared spectroscopy technique is potentially useful for screening polished rice samples for AFB1 contamination.

Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes.

The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithms.

Design of Electronic Nose System Using Gas Chromatography Principle and Surface Acoustic Wave Sensor

Most gases are odorless, colorless and also hazard to be sensed by the human olfactory system. Hence, an electronic nose system is required for the gas classification process. This study presents the design of electronic nose system using a combination of Gas Chromatography Column and a Surface Acoustic Wave (SAW). The Gas Chromatography Column is a technique based on the compound partition at a certain temperature. Whereas, the SAW sensor works based on the resonant frequency change. In this study, gas samples including methanol, acetonitrile, and benzene are used for system performance measurement. Each gas sample generates a specific acoustic signal data in the form of a frequency change recorded by the SAW sensor. Then, the acoustic signal data is analyzed to obtain the acoustic features, i.e. the peak amplitude, the negative slope, the positive slope, and the length. The Support Vector Machine (SVM) method using the acoustic feature as its input parameters are applied to classify the gas sample. Radial Basis Function is used to build the optimal hyperplane model which devided into two processes i.e., the training process and the external validation process. According to the result performance, the training process has the accuracy of 98.7% and the external validation process has the accuracy of 93.3%. Our electronic nose system has the average sensitivity of 51.43 Hz/mL to sense the gas samples.

Development and validation of a scoring index to predict the presence of lesions in capsule endoscopy in patients with suspected Crohn's disease of the small bowel: a Spanish multicenter study.

Capsule endoscopy (CE) is the first-line investigation in cases of suspected Crohn's disease (CD) of the small bowel, but the factors associated with a higher diagnostic yield remain unclear. Our aim is to develop and validate a scoring index to assess the risk of the patients in this setting on the basis of biomarkers. Data on fecal calprotectin, C-reactive protein, and other biomarkers from a population of 124 patients with suspected CD of the small bowel studied by CE and included in a PhD study were used to build a scoring index. This was first used on this population (internal validation process) and after that on a different set of patients from a multicenter study (external validation process). An index was designed in which every biomarker is assigned a score. Three risk groups have been established (low, intermediate, and high). In the internal validation analysis (124 individuals), patients had a 10, 46.5, and 81% probability of showing inflammatory lesions in CE in the low-risk, intermediate-risk, and high-risk groups, respectively. In the external validation analysis, including 410 patients from 12 Spanish hospitals, this probability was 15.8, 49.7, and 80.6% for the low-risk, intermediate-risk, and high-risk groups, respectively. Results from the internal validation process show that the scoring index is coherent, and results from the external validation process confirm its reliability. This index can be a useful tool for selecting patients before CE studies in cases of suspected CD of the small bowel.

The SWPER index for women's empowerment in Africa: development and validation of an index based on survey data.

SummaryBackgroundThe Sustainable Development Goals strongly focus on equity. Goal 5 explicitly aims to empower all women and girls, reinforcing the need to have a reliable indicator to track progress. Our objective was to develop a novel women's empowerment indicator from widely available data sources, broadening opportunities for monitoring and research on women's empowerment.MethodsWe used Demographic and Health Survey data from 34 African countries, targeting currently partnered women. We identified items related to women's empowerment present in most surveys, and used principal component analysis to extract the components. We carried out a convergent validation process using coverage of three health interventions as outcomes; and an external validation process by analysing correlations with the Gender Development Index.Findings15 items related to women's empowerment were selected. We retained three components (50% of total variation) which, after rotation, were identified as three dimensions of empowerment: attitude to violence, social independence, and decision making. All dimensions had moderate to high correlation with the Gender Development Index. Social independence was associated with higher coverage of maternal and child interventions; attitude to violence and decision making were more consistently associated with the use of modern contraception.InterpretationThe index, named Survey-based Women's emPowERment index (SWPER), has potential to widen the research on women's empowerment and to give a better estimate of its effect on health interventions and outcomes. It allows within-country and between-country comparison, as well as time trend analysis, which no other survey-based index provides.FundingBill & Melinda Gates Foundation.

SimAthens: A spatial microsimulation approach to the estimation and analysis of small area income distributions and poverty rates in the city of Athens, Greece

Published during a severe economic crisis, this study presents the first spatial microsimulation model for the analysis of income inequalities and poverty in Greece. First, we present a brief overview of the method and discuss its potential for the analysis of multidimensional poverty and income inequality in Greece. We then present the SimAthens model, based on a combination of small-area demographic and socioeconomic information available from the Greek census of population with data from the European Union Statistics on Income and Living Conditions (EU-SILC). The model is based on an iterative proportional fitting (IPF) algorithm, and is used to reweight EU-SILC records to fit in small-area descriptions for Athens based on 2001 and 2011 censuses. This is achieved by using demographic and socioeconomic characteristics as constraint variables. Finally, synthesis of the labor market and occupations are chosen as the main variables for externally validating our results, in order to verify the integrity of the model. Results of this external validation process are found to be extremely satisfactory, indicating a high goodness of fit between simulated and real values. Finally, the study presents a number of model outputs, illustrating changes in social and economic geography, during a severe economic crisis, offering a great opportunity for discussing further potential of this model in policy analysis.

Cost and effectiveness of omega-3 fatty acid supplementation in Chinese ICU patients receiving parenteral nutrition.

Background and objectivesClinical evidence supports the use of omega-3 polyunsaturated fatty acid (PUFA)-enriched lipid emulsions in place of standard lipid emulsions in parenteral nutrition (PN) for intensive care unit (ICU) patients, but uptake may be limited by higher costs. We compared clinical and economic outcomes for these two types of lipid emulsion in the Chinese ICU setting.MethodsWe developed a pharmacoeconomic discrete event simulation model, based on efficacy data from an international meta-analysis and patient characteristics, resource consumption, and unit costs from a Chinese institutional setting. Probabilistic sensitivity analyses were undertaken to assess the effects of uncertainty around input parameters. Model predictive validity was assessed by comparing results with data observed in a patient subset not used in the modeling.ResultsThe model predicted that omega-3 PUFA-enriched emulsion (Omegaven® 10% fish oil emulsion) would dominate standard lipid emulsions, with better clinical outcomes and lower overall health care costs (mean savings ~10,000 RMB), mainly as a result of faster recovery and shorter hospital stay (by ~6.5 days). The external validation process confirmed the reliability of the model predictions.ConclusionOmega-3 PUFA-enriched lipid emulsions improved clinical outcome and decreased overall costs in Chinese ICU patients requiring PN.