Split-sample Approach Research Articles

Drinking patterns, alcohol-related harm and views on policies: results from a pilot of the International Alcohol Control Study in Canada.

We conducted a pilot assessment of the feasibility of implementing the International Alcohol Control (IAC) Study in Ontario, Canada, to allow for future comparisons on the impacts of alcohol control policies with a number of countries. The IAC Study questionnaire was adapted for use in the province of Ontario, and a split-sample approach was used to collect data. Data were collected by computer-assisted telephone interviewing of 500 participants, with half the sample each answering a subset of the adapted IAC Study survey. Just over half of the sample (53.6%) reported high frequency drinking (once a week or more frequently), while 6.5% reported heavy typical occasion drinking (8 drinks or more per session). Self-reported rates of alcohol-related harms from one's own and others' drinking were relatively low. Attitudes towards alcohol control varied. A substantial majority supported more police spot checks to detect drinking and driving, while restrictions on the number of alcohol outlets and increases in the price of alcohol were generally opposed. This pilot study demonstrated that the IAC Study survey can be implemented in Canada with some modifications. Future research should assess how to improve participation rates and the feasibility of implementing the longitudinal aspect of the IAC Study. This survey provides additional insight into alcohol-related behaviours and attitudes towards alcohol control policies, which can be used to develop appropriate public health responses in the Canadian context.

Does attribute order influence attribute-information processing in discrete choice experiments?

The existing empirical evidence shows that both contingent valuation and discrete choice experiment (DCE) methods are susceptible to various ordering effects. However, very few studies have analysed attribute-ordering effects in DCEs, and no study has investigated their potential influence on information-processing strategies, such as attribute non-attendance (ANA). This paper tests for attribute-ordering effects and examines whether the order of attributes describing the alternatives affects respondents’ propensity to attend to or ignore an attribute. A split-sample approach is used, where one sample received a DCE version in which the positions of the first and last non-monetary attributes are switched across the sequence of choice tasks compared with the other sample. The results show that attribute order does not affect welfare estimates in a significant way under the standard assumption of full attribute attendance, thus rejecting the notion of procedural bias. However, the welfare estimates for the attributes whose order was reversed and the share of respondents who ignored them differ significantly between the two attribute-ordering treatments once ANA behaviour is accounted for in the estimated choice models. These results highlight the important role of information-processing strategies in the design and evaluation of DCEs.

Improving the Modeling of the Height–Diameter Relationship of Tree Species with High Growth Variability: Robust Regression Analysis of Ochroma pyramidale (Balsa-Tree)

Ochroma pyramidale (Cav. ex. Lam.) Urb. (balsa-tree) is a commercially important tree species that ranges from Mexico to northern Brazil. Due to its low weight and mechanical endurance, the wood is particularly well-suited for wind turbine blades, sporting equipment, boats and aircrafts; as such, it is in high market demand and plays an important role in many regional economies. This tree species is also well-known to exhibit a high degree of variation in growth. Researchers interested in modeling the height–diameter relationship typically resort to using ordinary least squares (OLS) to fit linear models; however, this method is known to suffer from sensitivity to outliers. Given the latter, the application of these models may yield potentially biased tree height estimates. The use of robust regression with iteratively reweighted least squares (IRLS) has been proposed as an alternative to mitigate the influence of outliers. This study aims to improve the modeling of height–diameter relationships of tree species with high growth variation, by using robust regressions with IRLS for data-sets stratified by site-index and age-classes. We implement a split sample approach to assess the model performance using data from Ecuador’s continuous forest inventory (n = 32,279 trees). A sensitivity analysis of six outlier scenarios is also conducted using a subsample of the former (n = 26). Our results indicate that IRLS regression methods can give unbiased height predictions. At face value, the sensitivity analysis indicates that OLS performs better in terms of standard error of estimate. However, we found that OLS suffers from skewed residual distributions (i.e., unreliable estimations); conversely, IRLS seems to be less affected by this source of bias and the fitted parameters indicate lower standard errors. Overall, we recommend using robust regression methods with IRLS to produce consistent height predictions for O. pyramidale and other tree species showing high growth variation.

Health-related quality of life and health utilities in insulin-treated type 2 diabetes: the impact of related comorbidities/complications.

To evaluate the impact of multiple comorbidities/complications on health-related quality of life (HRQoL) and health utilities in insulin-treated type 2 diabetes (T2DM). In a non-interventional, epidemiological study, data were collected from medical records and via interviews for 938 subjects from various geographical areas of Greece. HRQoL and health utilities were explored with the EQ-5D-5L. Univariate associations were evaluated with the Mann-Whitney and Kruskal-Wallis tests for continuous and Chi-squared tests for nominal variables, and binary logistic regressions were employed to obtain marginal effects. Employing a split sample approach, various specifications of ordinary least squares regression models were evaluated in terms of goodness of fit, model specification, shrinkage and predictive and discriminative performance, to select the best model for mapping health utilities using the whole dataset. Overall, the most important factors of impaired HRQoL and health utilities were higher age, female gender, obesity, poor glycemic control and increased duration of insulin treatment. History and increasing concurrence of all complications assessed were associated with exacerbated HRQoL problems, decreased health utilities and diminished health state, although it was not always statistically significant. The highest disutilities were associated with stroke (- 0.082), diabetic retinopathy (- 0.066), diabetic neuropathy (- 0.051) and severe hypoglycemia (- 0.050). The deleterious impact of comorbidities on insulin-dependent T2DM subjects' HRQoL has been confirmed and clinicians should adapt the priorities of disease management accordingly. The derived health utility estimates may be valuable for conducting economic evaluations of interventions in the area of T2DM when data are not available.

Italian validation of the caregiving tasks in multiple sclerosis scale (CTiMSS).

Informal caregivers provide fundamental help to persons with multiple sclerosis through a variety of tasks ranging from practical assistance to psychological support. Caregiving Tasks in Multiple Sclerosis Scale (CTiMSS) is a reliable and valid measure assessing the complex structure of caregiving tasks within the context of multiple sclerosis. The present study was aimed to test the validity and reliability of the Italian translation of CTiMSS in a sample of informal caregivers of persons with multiple sclerosis. A total of 580 caregivers (51.4% women, aged 18-81, M = 46.47, SD = 12.8) together with their care recipients (63.1% women, aged 19-60, M = 40.08, SD = 9.9) were involved in this study. Exploratory and confirmatory factor analyses with a split-sample approach were employed to evaluate construct validity. Convergent and discriminant validity were assessed using correlation indices with scales from Short Form Health Survey-36 and Positive and Negative Affect Schedule. Cronbach's alphas were used as a measure of reliability. Compared with the original CTiMSS 4-factor structure, a more stable and valid solution with 3 first-order factor loading on a single second-order factor was evaluated and confirmed; convergent and discriminant validity were also supported, and Cronbach's alpha values highlighted good-to-excellent reliability levels. Results supported the validity and reliability of the Italian CTiMSS. Thanks to its ability to assess specific caregiving tasks, the measure can be a useful instrument for tailoring intervention focused on the promotion of both caregivers' and care recipients' quality of life.

Maize Evapotranspiration Estimation Using Penman-Monteith Equation and Modeling the Bulk Canopy Resistance

Some techniques, such as the Katerji and Perrier approach, estimate the bulk canopy resistance (rc) as a function of meteorological variables and then calculate the hourly evapotranspiration using the Penman–Monteith equation, so that traditional crop coefficients are not needed. As far as we know, there are no published studies regarding using this method for a maize crop. The objective of this study was to calibrate and validate the canopy resistance for an irrigated continuous maize crop in the Midwestern United States (US). In addition, we determined the effect of derivation year, bowen ratio, and the extent of canopy. In this study we derive empirical coefficients necessary to estimate rc for maize, five years (2001–2005) were considered. A split-sample approach was taken, in which each year’s data was taken as a potential calibration data set and validation was accomplished while using the other four years of data. We grouped the data by green leaf area index (GLAI) and the Bowen ratio (β) by parsing the data into a 3 × 3 grouping: LAI (≥2, ≥3, and ≥4) and |β| (≤0.1, ≤0.2, and ≤0.3). The best fit data indicated reasonably good results for all nine groupings, so that the calibration coefficients derived for the conditions LAI ≥ 2 and |β| ≤ 0.3 were taken in light of the longer span associated with LAI ≥ 2 and the larger number of hours. For the calibrations in this subgroup, the results indicate that the annual empirical coefficients for rc are nearly the same and equally effective, regardless of the year used for calibration. Our validation included all the daytime hours regardless of β. Thus, it was concluded that the calibration at our site was independent of the derivation year. Knowledge of the Bowen ratio was useful in calibration, but accurate ET estimates (validation) can be obtained without knowledge of the Bowen ratio. Validation resulted in hourly ET estimates for irrigated maize that explained 83% to 86% of the variation in measured ET with an accuracy of ± 0.2 mm.

Validation of a Cyclic Algorithm to Proxy Number of Lines of Systemic Cancer Therapy Using Administrative Data.

Researchers are automating the process for identifying the number of lines of systemic cancer therapy received by patients. To date, algorithm development has involved manual modifications to predefined classification rules. In this study, we propose a supervised learning algorithm for determining the best-performing proxy for number of lines of therapy and validate this approach in four patient groups. We retrospectively analyzed BC Cancer pharmacy records from patients' cancer diagnosis until end of follow-up (cohort-specific, 2014/2015). We created and validated a cyclic algorithm in patients with advanced cancers of varying histologies, diffuse large B-cell lymphoma, follicular lymphoma, and chronic lymphocytic leukemia. To assess internal and external validity, we used a split-sample approach for all analyses and considered lines of therapy identified through manual review as our criterion standard. We measured agreement using correlation coefficients, mean squared error, nonparametric hypothesis testing, and quantile-quantile plots. Cohorts comprised 91 patients with advanced cancers, 121 with chronic lymphocytic leukemia, 440 with follicular lymphoma, and 679 with diffuse large B-cell lymphoma. Number of lines of therapy received and patients' treatment period length varied substantially across cohorts. Despite these differences, our algorithm successfully identified a best-performing proxy for number of lines of therapy for each cohort, which was moderate to highly correlated with (within-sample: 0.73 ≤ Pearson correlation ≤ 0.84; out-of-sample: 0.52 ≤ Pearson correlation ≤ 0.76) and whose distribution did not significantly differ from the criterion standard within or out of sample (P > .10). Supervised learning is an ideal tool for generating a best-performing proxy that recognizes prescription drug patterns and approximates number of lines of therapy. Our cyclic approach can be used in jurisdictions with access to administrative pharmacy data.

Development of risk models for major adverse chronic renal outcomes among patients with type 2 diabetes mellitus using insurance claims: a retrospective observational study

Objective: To develop and validate models allowing the prediction of major adverse chronic renal outcomes (MACRO) in patients with type 2 diabetes mellitus (T2DM) using insurance claims data.Methods: The Optum Integrated Real World Evidence Electronic Health Records and Claims de-identified database (10/01/2006–09/30/2016) was used to identify T2DM patients ≥50 years old. Risk factors were assessed over a 12-month baseline period, and MACRO were subsequently assessed until the end of data availability, continuous enrollment, or death. Separate models were built for moderate-to-severe diabetic kidney disease (DKD), end-stage renal disease (ESRD), and renal death. A random split-sample approach was employed, where 70% of the sample served for model development (training set) and the remaining 30% served for validation (testing set). C-statistics were used to assess model performance.Results: A total of 160,031 patients were included. Risk factors associated with MACRO for all models included adapted diabetes complications severity index, heart failure, anemia, diabetic nephropathy, and CKD. C-statistics ranged between 0.70 (moderate-to-severe DKD) and 0.84 (renal death) in the testing set. A substantial proportion (e.g. 88.7% for moderate-to-severe DKD) of patients predicted to be at high-risk of MACRO did not have diabetic nephropathy, proteinuria, or CKD at baseline.Conclusions: The models developed using insurance claims data could reliably predict the risk of MACRO in patients with T2DM and enabled patients at higher-risk of DKD to be identified in the absence of baseline diabetic nephropathy, CKD, or proteinuria. These models could help establish strategies to reduce the risk of MACRO in T2DM patients.

Comparison of four learning-based methods for predicting groundwater redox status

Knowing the location where groundwater denitrification occurs, or by proxy the groundwater redox status (oxic, mixed, and anoxic), is valuable information for assessing and managing potential agricultural land-use impacts on freshwater quality. We compare the efficacy of supervised (Linear Discriminant Analysis LDA; Boosted Regression Trees, BRT; and Random Forest, RF) and unsupervised (Modified Self-Organizing Map, MSOM) learning-based methods to predict groundwater redox status in the agriculturally dominated Tasman, Waikato, and Wellington regions of New Zealand. Thresholds applied to regional groundwater-quality samples provide redox status variables and learn heuristics constrained by these variables and applied to spatial factors (climate, elevation, geologic, hydrology soils, and well depth) identify optimal sets of regional predictor variables. A split-sample approach is used to train and test the learning methods ability to predict redox status using the optimal predictor variables. Overall, the supervised methods demonstrate a prediction bias toward oxic conditions and inability to perform statistically well when using independent regional data; for example, consider kappa statistics for BRT (Tasman: 0.42, Waikato: 0.38, Wellington: 0.17), RF (Tasman: 0.42, Waikato: 0.47, Wellington: 0.17 and LDA (Tasman: 0.46, Waikato: 0.32, Wellington: 0.17). By contrast, the unsupervised method performs statistically well when predicting oxic, mixed, and anoxic conditions and corresponding depths when using independent regional data; for example, consider MSOM kappa statistics for Tasman: 0.78, Waikato: 0.80, Wellington: 0.76. The unsupervised learning method provides the added benefits of being (1) able to combine predictions into 3D regional anoxic probability plots for interpreting the spatial influence of paleosols and groundwater flowpaths on redox status, and (2) readily extended to map 3D redox status across New Zealand and other countries despite data bias and sparsity.

Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data.

The biomass of three agricultural crops, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), was studied using multi-temporal dual-polarimetric TerraSAR-X data. The radar backscattering coefficient sigma nought of the two polarization channels HH and VV was extracted from the satellite images. Subsequently, combinations of HH and VV polarizations were calculated (e.g. HH/VV, HH + VV, HH × VV) to establish relationships between SAR data and the fresh and dry biomass of each crop type using multiple stepwise regression. Additionally, the semi-empirical water cloud model (WCM) was used to account for the effect of crop biomass on radar backscatter data. The potential of the Random Forest (RF) machine learning approach was also explored. The split sampling approach (i.e. 70% training and 30% testing) was carried out to validate the stepwise models, WCM and RF. The multiple stepwise regression method using dual-polarimetric data was capable to retrieve the biomass of the three crops, particularly for dry biomass, with R2 > 0.7, without any external input variable, such as information on the (actual) soil moisture. A comparison of the random forest technique with the WCM reveals that the RF technique remarkably outperformed the WCM in biomass estimation, especially for the fresh biomass. For example, the R 2 > 0.68 for the fresh biomass estimation of different crop types using RF whereas WCM show R 2 < 0.35 only. However, for the dry biomass, the results of both approaches resembled each other.

Abusive supervision, co-worker abuse and work outcomes: procedural justice as a mediator

PurposeBased on the social exchange theory and the reactance theory, the purpose of this paper is to investigate the effects of workplace abuse from two sources. The study explores the linkage between abusive supervision and co-worker abuse on the targeted employee’s organizational commitment, organizational citizenship behavior and intention to quit as mediated by procedural justice. Furthermore, this study extends understanding workplace abuse consequences by investigating its effects on organizational citizenship behavior directed to individuals and organizational citizenship behavior directed to the organization.Design/methodology/approachTo test the proposed hypotheses, a cross-sectional research design was used. The sample comprised 500 employees working in various private and public sector organizations in the Republic of Trinidad and Tobago. Using a split-sample approach, mediation analyses were performed on the test and validation samples.FindingsThe research results showed that procedural justice mediated the relationship between abusive supervision and affective and normative commitment, organizational citizenship behavior directed to individuals and intention to quit. Procedural justice was found to mediate the relationship between co-worker abuse and affective and normative commitment, and intention to quit.Originality/valueThis study extends previous academic studies on workplace abuse by comparing the effects of abusive supervision and the lesser researched source of co-worker abuse on the targeted employee’s organizational commitment, organizational citizenship behavior and intention to quit. It also reports on the effects of each source on an employee’s organizational citizenship behavior directed to individuals and organizational citizenship behavior directed to the organization, as there is limited empirical research within the workplace abuse literate on these two dimensions.

Climate model bias correction for nonstationary conditions

In most of the climate change impact assessment studies, climate model bias is considered to be stationary between the control and scenario periods. Few methods are found in the literature that addresses the issue of nonstationarity in correcting the bias. To overcome the shortcomings reported in these approaches, three new methods of bias correction (NBC_μ, NBC_σ, and NBC_bs) are presented. The methods are improvised versions of previous techniques relying on distribution mapping. The methods are tested using split sample approach over 50-year historical period for nine climate stations in Ontario, using six regional climate models. The average bias reduction improvement by new methods, in mean daily and monthly precipitation, was found to be 73.9%, 74.3%, and 77.4%, respectively, higher than that obtained by the previous methods (eQM 67.7% and CNCDFm_NP 64.1%). Thus, the methods are found to be more effective in accounting for nonstationarity in the model bias.

Individual Nurse Productivity in Preparing Patients for Discharge Is Associated With Patient Likelihood of 30-Day Return to Hospital.

Applied to value-based health care, the economic term "individual productivity" refers to the quality of an outcome attributable through a care process to an individual clinician. This study aimed to (1) estimate and describe the discharge preparation productivities of individual acute care nurses and (2) examine the association between the discharge preparation productivity of the discharging nurse and the patient's likelihood of a 30-day return to hospital [readmission and emergency department (ED) visits]. Secondary analysis of patient-nurse data from a cluster-randomized multisite study of patient discharge readiness and readmission. Patients reported discharge readiness scores; postdischarge outcomes and other variables were extracted from electronic health records. Using the structure-process-outcomes model, we viewed patient readiness for hospital discharge as a proximal outcome of the discharge preparation process and used it to measure nurse productivity in discharge preparation. We viewed hospital return as a distal outcome sensitive to discharge preparation care. Multilevel regression analyses used a split-sample approach and adjusted for patient characteristics. A total 522 nurses and 29,986 adult (18+ y) patients discharged to home from 31 geographically diverse medical-surgical units between June 15, 2015 and November 30, 2016. Patient discharge readiness was measured using the 8-item short form of Readiness for Hospital Discharge Scale (RHDS). A 30-day hospital return was a categorical variable for an inpatient readmission or an ED visit, versus no hospital return. Variability in individual nurse productivity explained 9.07% of variance in patient discharge readiness scores. Nurse productivity was negatively associated with the likelihood of a readmission (-0.48 absolute percentage points, P<0.001) and an ED visit (-0.29 absolute percentage points, P=0.042). Variability in individual clinician productivity can have implications for acute care quality patient outcomes.

Revisiting cost vector effects in discrete choice experiments

The estimation of marginal utility of income in discrete choice experiments is of crucial importance for the estimation of willingness to pay (WTP) and welfare estimates. Despite this central importance, there are only few investigations into the impact of the design of the cost attribute vector on choices and WTP estimates. We present a conceptual framework that describes why cost vector effects might occur in choice experiments, and investigate cost vector effects empirically drawing on data from a choice experiment in the context of peatland restoration in Scotland. This study employs a split sample approach with three different cost vectors that vary considerably in the cost levels offered to respondents, and investigates differences between treatments with respect to marginal WTP estimates, status quo choice, use of systematic decision strategies and attribute non-attendance. A key finding is that the choice of cost vectors can affect the incidence of decision strategies. After accounting for the differential use of a decision strategy that might not be consistent with random utility modelling, cost vectors that are higher in magnitude result in higher WTP, in line with an anchoring hypothesis. We find weak support that marginal WTP of lower income respondents is affected differently compared to higher income respondents through the use of different cost vectors. Differences in welfare estimates resulting from the use of different cost vectors might change outcomes of cost-benefit analyses. We therefore recommend that researchers include tests of sensitivity of welfare estimates to different cost vectors in their study design.

Metabolomic markers of healthy dietary patterns in US postmenopausal women

Healthy diet patterns are associated with lower risk of cancer and other chronic diseases. Metabolomics has the potential to expand dietary biomarker development to include dietary patterns, which may provide a complement or alternative to self-reported diet. This study examined the correlation of serum untargeted metabolomic markers with 4 diet pattern scores-the alternate Mediterranean diet score (aMED), alternate Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and the Healthy Eating Index (HEI)-2015-and used multivariate methods to identify discriminatory metabolites for each pattern. Among 1367 US postmenopausal women with serum metabolomic data in the Cancer Prevention Study-II Nutrition Cohort, we conducted partial correlation analysis, adjusted for demographic and lifestyle variables, to examine cross-sectional correlations between serum metabolomic markers and healthy diet pattern scores. In a randomly selected "training" set (50%), we conducted orthogonal partial least-squares discriminant analysis to identify metabolites that discriminated the top from bottom diet score quintiles. Combinations of metabolites with a variable importance in projection (VIP) score ≥2.5 were tested for predictability in the "testing" set based on the use of receiver operating characteristic curves. Out of 1186 metabolites, 32 unique metabolites were considered discriminatory based on a VIP score ≥2.5 in the training dataset with some overlap across scores (aMED=16; AHEI=17; DASH=13; HEI=12). Spearman partial correlation analyses, applying a cut-point (|r|≥0.15) and Bonferroni correction (P<1.05×10-5), identified similar key metabolites. The top 5 metabolites for each pattern mostly distinguished high compared with low scores; 4 of the 5 (fish-derived) metabolites were the same for aMED and AHEI, 2 of which were identified for HEI; 4 DASH metabolites were unique. Metabolomic methods that used a split-sample approach identified potential biomarkers for 4 healthy diet patterns. Similar metabolites across scores reflect fish consumption in healthy dietary patterns. These findings should be replicated in independent populations.

Consumer Preferences for Different Designs of Carbon Footprint Labelling on Tomatoes in Germany—Does Design Matter?

The climate impact of tomato production is an important issue in the sustainability of tomatoes, especially in northern European countries, such as Germany. Communicating the climate impact of products to the consumer is difficult and the design of the label might be the key to its success. For this reason, the present study compares the utilities of six different carbon footprint labels to evaluate which label design works best for the consumer. 598 consumers were surveyed in a representative online choice-experiment. The participants had to choose between tomatoes with different product characteristics, such as origin, price, organic label, and carbon footprint label. A split sample approach was used where each sub-sample with around n = 100 saw a different carbon footprint label design in the choice-experiment. The results suggest that qualitative carbon footprint labels using color-coded traffic light labelling are superior to those that claim climate impact reduction or neutrality, including those that provide more details regarding the climate impact of the product and the company. The latent class analysis with four consumer segments shows that a significant proportion of consumers in Germany would consider a carbon footprint label as an important characteristic.

Prevalence, characteristics, and risk factors of occult uterine cancer in presumed benign hysterectomy

Occult uterine cancer at the time of benign hysterectomy poses unique challenges in patient care. There is large variability and uncertainty in estimated risk of occult uterine cancer in the literature and prior research often did not differentiate/include all subtypes. To thoroughly examine the prevalence of occult uterine cancer in a large population-based sample of women undergoing hysterectomy for presumed benign indications and to identify associated risk factors. Using the New York Statewide Planning and Research Cooperative System database, we identified 229,536 adult women who underwent an inpatient or outpatient hysterectomy for benign indications during the period October 1, 2003 to December 31, 2013 at civilian hospitals and ambulatory surgery centers throughout the state. Diagnosis of corpus uteri cancer within 28 days after the index hysterectomy was determined using linked state cancer registry data. We estimated the prevalence of occult uterine cancer (overall and by subtype) and developed and validated risk prediction models using a random split sample approach. Overall, 0.96% (95% confidence interval: 0.92-1.00%) of the women had occult uterine cancer, including 0.75% (95% confidence interval: 0.71-0.78%) with endometrial carcinoma and 0.22% (95% confidence interval: 0.20-0.23%) with uterine sarcoma. The prevalence of leiomyosarcoma was 0.15% (95% confidence interval: 0.13-0.17%). Seventy-one percent of the endometrial carcinomas and 58.0% of the uterine sarcomas were at localized stage. The risk for occult uterine cancer ranged from 0.10% in women aged 18-29 years to 4.40% in women aged ≥75 years; and varied from 0.14% in women undergoing hysterectomy for endometriosis to 0.62% for uterine fibroids and 8.43% for postmenopausal bleeding. The risk of occult uterine cancer was also significantly associated with race/ethnicity, obesity, comorbidity, and personal history of malignancy. Prediction models incorporating these risk factors had high negative predictive values (99.8% for endometrial carcinoma and 99.9% for uterine sarcoma) and good rule-out accuracy despite low positive predictive value. In women undergoing hysterectomy for presumed benign indications, 0.96% had unexpected uterine cancer. Patient characteristics such as age, surgical indication, and medical history may help guide risk stratification.

Italian Validation of the Meaning in Life Questionnaire: Factor Structure, Reliability, Convergent, and Discriminant Validity.

Several studies highlighted the role of meaning in life as a major component of well-being and researchers have developed different measures to assess the features of this construct. In the present study, the psychometric properties of the Meaning in Life Questionnaire were investigated in the Italian context. The Meaning in Life Questionnaire is a 10-item scale measuring perceived presence of and search for meaning in life, conceptualized as two separate factors. The former refers to perceived meaning and purpose in life, the latter to the active commitment to find meaning in life. Participants were 464 adults aged 20 to 60 years ( M = 39.34; SD = 10.86; 54.7% women). Factor structure was inspected through exploratory and confirmatory factor analyses using a split-sample approach. Internal consistency was assessed through Cronbach's alphas, interitem and item-scale correlations. Convergent and discriminant validity with measures of well-being, personality, mental health, and physical health were also evaluated. Factor analyses supported the adequacy of the Meaning in Life Questionnaire two-factor structure in the Italian context; internal consistency measures corroborated the measure's reliability; and correlation matrix coefficients sustained convergent and discriminant validity. Results showed that the Meaning in Life Questionnaire is a valid and reliable measure to assess meaning in life and its relationship with well-being within the Italian context.

May a Regulatory Incentive Increase WTP for Cars with a Fuel Efficiency Label? Estimating Regulatory Costs Through a Split-Sample DCE in New Delhi, India

The Indian government is considering the adoption of fuel efficiency labels for cars. By means of a Discrete Choice Experiment (DCE), this paper assesses New Delhi's car buyers' preferences for such a label. Random parameters specifications yield a willingness to pay (WTP) of 4.93 thousand US dollars for a car with the best efficiency label. As a novelty, and by means of a split-sample approach, we test whether this WTP would increase due to an incentive described as a regulation restricting the number of days a car can be driven weekly unless the car is awarded the best efficiency label --- New Delhi's residents experienced a similar policy in January and April, 2016. This incentive increases the WTP by 51.5% --- from 4.93 to 7.48 thousand US dollars. These extra 2.55 thousand US dollars can be interpreted as regulatory costs and are close to the 2.96 thousand US dollars implicit in estimates reported by Blackman et al. (2018) in the context of the Mexican Hoy No Circula.

Detecting moderate or complex congenital heart defects in adults from an electronic health records system.

The prevalence of moderate or complex (moderate-complex) congenital heart defects (CHDs) among adults is increasing due to improved survival, but many patients experience lapses in specialty care or their CHDs are undocumented in the medical system. There is, to date, no efficient approach to identify this population. To develop and assess the performance of a risk score to identify adults aged 20-60 years with undocumented specific moderate-complex CHDs from electronic health records (EHR). We used a case-control study (596 adults with specific moderate-complex CHDs and 2384 controls). We extracted age, race/ethnicity, electrocardiogram (EKG), and blood tests from routine outpatient visits (1/2009 through 12/2012). We used multivariable logistic regression models and a split-sample (4: 1 ratio) approach to develop and internally validate the risk score, respectively. We generated receiver operating characteristic (ROC) c-statistics and Brier scores to assess the ability of models to predict the presence of specific moderate-complex CHDs. Out of six models, the non-blood biomarker model that included age, sex, and EKG parameters offered a high ROC c-statistic of 0.96 [95% confidence interval: 0.95, 0.97] and low Brier score (0.05) relative to the other models. The adult moderate-complex congenital heart defect risk score demonstrated good accuracy with 96.4% sensitivity and 80.0% specificity at a threshold score of 10. A simple risk score based on age, sex, and EKG parameters offers early proof of concept and may help accurately identify adults with specific moderate-complex CHDs from routine EHR systems who may benefit from specialty care.