Logistic Probability Model Research Articles

Variability in Kidney Cancer Treatment and Survival in England: Results of a National Cohort Study

AimsTo establish whether there are geographic differences in treatments and outcomes for patients with kidney cancer (KC) in England which could potentially be improved by the creation of national guidelines. Materials and methodsA multidisciplinary group convened by the charity Kidney Cancer UK developed Quality Performance Indicators (QPIs) for the treatment of KC. Adherence to these QPIs was reported for all patients with a histological diagnosis of KC diagnosed in England between 2017 and 2018. Utilising data extracted from national datasets, logistic and linear probability models were used to estimate geographic variation in the delivery of surgery and systemic anti-cancer therapy at Cancer Alliance and NHS trust levels. Results were adjusted for a priori confounders, including age at diagnosis, area deprivation of residence, and Charlson Comorbidity Index. Differences in overall survival are reported. ResultsThe cohort comprised 18,640 tumours in 18,421 patients. Of tumours diagnosed, median patient age was 68 (interquartile range 58–77) years and 63.4% were in males. When stratified by Cancer Alliance, the proportions of T1a/T1b/N0/M0 KC that had radical nephrectomy (RN), nephron sparing surgery or ablation ranged from 53.3% (95% CI [48.7, 57.8]) to 80.3% (95% CI [73.0, 86.0]). For stage T1b-3 cancers, the proportion that received RN ranged from 65.6% (95% CI [60.3, 70.5]) to 77.3% (95% CI [72.1, 81.7]). Patients with M0 (n = 12,365) and M1 KC (n = 3312) at diagnosis had 24-month survival of 87.5% and 25.1%, respectively. Of patients diagnosed with M1 KC, 50.3% received systemic anti-cancer therapy, ranging from 39.7% (95% CI [33.7, 46.1]) to 70.7% (95% CI [59.6, 79.8]) between Cancer Alliances. The six-month survival of these patients was 77.4% compared to 27.6% for those that did not receive SACT. ConclusionThese major geographical differences in surgical and systemic therapy practice have led to national guideline development.

Criticality-Aware Negotiation-Driven Scrubbing Scheduling for Reliability Maximization in SRAM-Based FPGAs

Memory scrubbing is a resource-efficient technique to ensure the high reliability of SRAM-based FPGAs by refreshing the configuration memory just before its execution. To maximize reliability, a scrubbing scheduling algorithm is expected to scrub as many tasks as possible. Unfortunately, contemporary scheduling algorithms either sub-optimally handle scrubbing conflicts under bursty requests from multiple user tasks or discriminate against low-criticality tasks by giving them very low scrubbing opportunities. Besides, exploring the architectural support for scrubbing problems may bring considerable potential for reliability improvements. However, this direction of scheduling-architecture co-optimization has not been well studied so far. In this paper, we propose a negotiation-based dynamic scrubbing framework, which addresses the abovementioned issues in three phases: First, we propose a negotiation-driven scrubbing scheduling algorithm, which temporarily allows and iteratively reduces the conflicts of scrubbing tasks in order to accommodate more scrubbing tasks to be scheduled. Second, we develop a logistic probability model to prevent scheduling starvation of a set of mix-criticality tasks by dynamically legalizing conflicting ones, considering both the criticality and schedulability of each task. Third, we develop a DVFS-based multi-ICAPs allocation algorithm to co-optimize with FPGA architectural features for reliability maximization. Compared to the state-of-the-art, experimental results show that our work achieves up to 31.46% improvement in terms of reliability for contemporary SRAM-based FPGAs.

Tropically Adapted Passive Building: A Descriptive-Analytical Approach Using Multiple Linear Regression and Probability Models to Predict Indoor Temperature

The quest for energy efficiency in buildings has placed a demand for designing and modeling energy-efficient buildings. In this study, the thermal energy performance of a tropically adapted passive building was investigated in the warm tropical climate of Malaysia. Two mock-up buildings were built to represent a “green”, made of clay brick double-glazed passive building and a conventional, made of concrete “red” building. The mean indoor temperature of the passive building was found to be always lower than that of the red building throughout the experiment during different weather constellations. Our research builds upon existing work in the field by combining multiple linear regression models and distribution models to provide a comprehensive analysis of the factors affecting the indoor temperature of a building. The results from the fitted multiple linear regression models indicate that walls and windows are critical components that considerably influence the indoor temperature of both passive buildings and red buildings, with the exception of passive buildings during the hot season, where the roof has a greater influence than the window. Furthermore, the goodness-of-fit test results of the mean indoor temperature revealed that the Fréchet and Logistic probability models fitted the experimental data in both cold and hot seasons. It is intended that the findings of this study would help tropical countries to devise comfortable, cost-effective passive buildings that are green and energy efficient to mitigate global warming.

Will Indonesia enter the 2023 financial crisis? Application of early warning model system

Purpose ― This paper estimates the possibility of a financial crisis in Indonesia using an early warning system (EWMS) model. Method ― A quantitative EWMS model has been developed to detect a potential financial crisis in 2023 based on the econometric logistic probability model (Logit) Findings ― Based on the model estimates, Indonesia is expected to enter a financial crisis without adequate macroeconomic policies in the next 12 to 24 months. In recent years, Indonesia has implemented prudent macroeconomic policies such as increasing the Bank Indonesia policy rate and sustaining the state budget to avoid the impact of a deep financial crisis. Implications ― To avoid the potential for further financial crises, Indonesia must implement a wider range of crisis mitigation policies. Originality/value ― Although many argue that financial crises are predictable, it has been demonstrated in the literature that little is known about how to prevent them. This paper contributes to providing empirical evidence to address these issues.

A data driven gully head susceptibility map of Africa at 30 m resolution

Predicting gully erosion at the continental scale is challenging with current generation models. Moreover, datasets reflecting gully erosion processes are still rather scarce, especially in Africa. This study aims to bridge this gap by collecting an extensive dataset and developing a robust, empirical model that predicts gully head density at high resolution for the African continent. We developed a logistic probability model at 30 m resolution that predicts the likelihood of gully head occurrence using currently available GIS data sources. To calibrate and validate this model, we used a new database of 31,531 gully heads, mapped over 1216 sites across Africa. The exact location of all gully heads was manually mapped by trained experts using high-resolution imagery available from Google Earth. This allowed the extraction of detailed information at the gully head scale, such as the local soil surface slope. Variables included in our empirical model are topography, climate, vegetation, soil characteristics and tectonic context. They are consistent with our current process-based understanding of gully formation and evolution. The model shows that gully occurrences mainly depend on slope steepness, soil texture and vegetation cover and to a lesser extent on rainfall intensity and tectonic activity. The combination of these factors allows for robust and fairly reliable predictions of gully head occurrences, with Areas Under the Curve for validation around 0.8. Based on these results, we present the first gully head susceptibility map for Africa at a 30 m resolution.

Examining the Item-level Psychometric Properties of the Self-Efficacy Questionnaire (SEQ) for Students in Communication Sciences and Disorders

The purpose of this study was to examine the psychometric properties of the Self-Efficacy Questionnaire (SEQ), a self-reported questionnaire using Rasch analysis, a one-parameter logistic probability model based on scientific measurement principles. Rasch analysis was completed using the rating scale model. The various analyses allow researchers to examine the item-level psychometric properties of the SEQ, which result in measures that provide evidence for validity, reliability, and sensitivity of the instrument. Rasch analysis demonstrated that the latent trait established by the SEQ, Perceived Self-efficacy (called self-efficacy from here on), was a unidimensional construct that could be measured on a linear scale. The instrument demonstrated sound item-level psychometric properties, including a wide span of item difficulty, along with limited ceiling and no floor effects. Person reliability was good, and the SEQ separated raters into at least three statistically different levels of self-efficacy. These results provide evidence for the SEQ's validity, reliability, and sensitivity. Based on this preliminary analysis, the SEQ demonstrated more than adequate item-level psychometric properties for use, although more research needs to be done. Further, instructors could use the SEQ to give preliminary information on whether or not a class assignment leads to increased self-efficacy in undergraduate and graduate students.

Statistical Framework for Western North Pacific Tropical Cyclone Landfall Risk through Modulation of the Western Pacific Subtropical High and ENSO

Abstract Seasonal predictions of tropical cyclone (TC) landfalls are challenging because seasonal landfall count not only depends on the number and spatial distribution of TC genesis, but also whether those TCs are steered toward land or not. Past studies have separately examined genesis and landfall as a function of large-scale ocean and atmospheric environmental conditions. Here, we introduce a practical statistical framework for estimating the seasonal count of TC landfalls as the product of a Poisson model for seasonal TC genesis and a logistic model for landfall probability. We compute spatial variations in TC landfall and genesis by decomposing TC activity in the western North Pacific (WNP) basin into 10° × 10° bins, then identify coherent regions where El Niño–Southern Oscillation (ENSO) and the western extent of the Pacific subtropical high (WPSH) have significant influences on seasonal landfall count. Our framework shows that ENSO and the WPSH are weakly related to basinwide landfalls but strongly related to regional genesis and landfall probability. ENSO modulates the zonal distribution of TC genesis, consistent with past work, whereas the WPSH modulates the meridional distribution of landfall probability due to variations in steering flow associated with the Pacific subtropical high. These spatial patterns result in four coherent subregions of the WNP basin that define seasonal landfall variations: landfall count increases in the southwestern WNP during a positive WPSH and La Niña, the south-central WNP during a positive WPSH and El Niño, the eastern WNP during a negative WPSH and El Niño, and the northern WNP during a negative WPSH and La Niña.

NTCP Modeling for High-Grade Temporal Radionecroses in a Large Cohort of Patients Receiving Pencil Beam Scanning Proton Therapy for Skull Base and Head and Neck Tumors

To develop a normal tissue complication probability model including clinical and dosimetric parameters for high-grade temporal lobe radionecroses (TRN) after pencil beam scanning proton therapy. We included data on 299 patients with skull base and head and neck tumors treated with pencil-beam scan proton therapy, with a total dose of ≥60 GyRBE (relative biological effectiveness) from May 2004 to November 2018. We considered 9 clinical and 27 dosimetric parameters for the structure-wise modeling of high-grade (grade ≥2) TRN. After eliminating strongly cross-correlated variables, we generated logistic regression models using least absolute shrinkage and selection operator regression. We performed bootstrapping to assess parameter selection robustness and evaluated model performance via cross-correlation by assessing the area under the curve of receiver operating characteristic curves and calibration with a Hosmer-Lemeshow test statistic. After a median radiologic follow-up of 51.5 months (range, 4-190), 27 patients (9%) developed grade ≥2 TRN. Eleven patients had bitemporal necrosis, resulting in 38 events in 598 temporal lobes for structure-wise analysis. During our bootstrapping analysis, we found that the highest selection frequency was for prescription dose, followed by age, V40Gy (%), hypertension, and dose to at least 1 cc (D1cc) (Gy) in the temporal lobe. During our cross-validation, we found that age*prescription-dose*D1cc (Gy)*hypertension was superior in all described test statistics. We built full cohort structure-wise and patient-wise models with maximum area under the curve of receiver operating characteristic curves of 0.79 (structure-wise) and 0.76 (patient-wise). While developing a logistic regression normal tissue complication probability model to predict grade ≥2 TRN, the best fit was found for the model containing age, prescription dose, D1cc (Gy), and hypertensive blood pressure as risk factors. External validation will be the next step to improve generalizability and potential introduction into clinical routine.

Modelling a new approach for radio-ablation after resection of breast ductal carcinoma in-situ based on the BAT-90 medical device

The majority of local recurrences, after conservative surgery of breast cancer, occurs in the same anatomical area where the tumour was originally located. For the treatment of ductal carcinoma in situ (DCIS), a new medical device, named BAT-90, (BetaGlue Technologies SpA) has been proposed. BAT-90 is based on the administration of 90Y β-emitting microspheres, embedded in a bio-compatible matrix. In this work, the Geant4 simulation toolkit is used to simulate BAT-90 as a homogenous cylindrical 90Y layer placed in the middle of a bulk material. The activity needed to deliver a 20 Gy isodose at a given distance z from the BAT-90 layer is calculated for different device thicknesses, tumour bed sizes and for water and adipose bulk materials. A radiobiological analysis has been performed using both the Poisson and logistic Tumour Control Probability (TCP) models. A range of radiobiological parameters (α and β), target sizes, and densities of tumour cells were considered. Increasing α values, TCP increases too, while, for a fixed α value, TCP decreases as a function of clonogenic cell density. The models predict very solid results in case of limited tumour burden while the activity/dose ratio could be further optimized in case of larger tumour beds.

Effects of the Medicaid expansion under the Affordable Care Act on health insurance coverage, health care access, and use for people with disabilities.

People with disabilities have higher health care needs, service utilization, and expenditures. They are also more likely to lack insurance and experience unmet need for medical care. There has been limited research on the effects of the Affordable Care Act Medicaid expansion on people with disabilities. To examine the effects of the Medicaid expansion on health insurance coverage, access, and service use for working-age adults with disabilities. A retrospective study using 11 years (2007-2017) of data from the Medical Expenditure Panel Survey - Household Components, linked to Area Health Resource Files and Local Area Unemployment Statistics (N=40,995). Difference-in-differences multinomial logistic and linear probability models with state and year fixed-effects were used to estimate the effects. We found strong evidence of increased Medicaid coverage in expansion states (3.2 to 5.0 percentage points), reasonably strong evidence of reduced private insurance coverage (-2.2 to-2.5 percentage points), and some evidence of reduced uninsured rate (from no effect to-3.7 percentage points). Results suggest that the increase in Medicaid coverage was due at least in part to the "crowd-out" of private insurance in expansion states. No statistically significant effects were detected for access and use outcomes. Findings suggest that state Medicaid expansions led to an increase in Medicaid coverage and a decrease in private insurance coverage as well as the uninsured. However, no evidence was found for health care access and use outcomes. Further research into access and use is needed when more data become available for the post-expansion period.

Determinants of substantial public debt reductions in Central and Eastern European Countries

Government debt development is a timeless issue in economics that has gained even more attention in light of the global financial crisis and the Covid 19 pandemic crisis. The following paper uses several specifications of a logistic probability model to examine the key determinants underlying substantial public debt reductions in Central and Eastern European EU Member States for the period 1996–2020. The results suggest that fiscal adjustments are more likely to be successful in reducing public debt if they are based on expenditure cuts rather than revenue increases. In this context, cuts in social benefits and government employee compensation prove to be particularly effective. In addition, favourable economic growth rates increase the probability of a substantial reduction in government debt.

Modelling hospital outcome: problems with endogeneity

BackgroundMortality modelling in the critical care paradigm traditionally uses logistic regression, despite the availability of estimators commonly used in alternate disciplines. Little attention has been paid to covariate endogeneity and the status of non-randomized treatment assignment. Using a large registry database, various binary outcome modelling strategies and methods to account for covariate endogeneity were explored.MethodsPatient mortality data was sourced from the Australian & New Zealand Intensive Society Adult Patient Database for 2016. Hospital mortality was modelled using logistic, probit and linear probability (LPM) models with intensive care (ICU) providers as fixed (FE) and random (RE) effects. Model comparison entailed indices of discrimination and calibration, information criteria (AIC and BIC) and binned residual analysis. Suspect covariate and ventilation treatment assignment endogeneity was identified by correlation between predictor variable and hospital mortality error terms, using the Stata™ “eprobit” estimator. Marginal effects were used to demonstrate effect estimate differences between probit and “eprobit” models.ResultsThe cohort comprised 92,693 patients from 124 intensive care units (ICU) in calendar year 2016. Patients mean age was 61.8 (SD 17.5) years, 41.6% were female and APACHE III severity of illness score 54.5(25.6); 43.7% were ventilated. Of the models considered in predicting hospital mortality, logistic regression (with or without ICU FE) and RE logistic regression dominated, more so the latter using information criteria indices. The LPM suffered from many predictions outside the unit [0,1] interval and both poor discrimination and calibration. Error terms of hospital length of stay, an independent risk of death score and ventilation status were correlated with the mortality error term. Marked differences in the ventilation mortality marginal effect was demonstrated between the probit and the "eprobit" models which were scenario dependent. Endogeneity was not demonstrated for the APACHE III score.ConclusionsLogistic regression accounting for provider effects was the preferred estimator for hospital mortality modelling. Endogeneity of covariates and treatment variables may be identified using appropriate modelling, but failure to do so yields problematic effect estimates.

Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer.

Standard treatment for locally advanced cervical cancer (LACC) is chemoradiotherapy followed by brachytherapy. Despite radiation therapy advances, the toxicity rate remains significant. In this study, we compared the prediction of toxicity events after radiotherapy for locally advanced cervical cancer (LACC), based on either dose-volume histogram (DVH) parameters or the use of a radiomics approach applied to dose maps at the voxel level. Toxicity scores using the Common Terminology Criteria for Adverse Events (CTCAE v4), spatial dose distributions, and usual clinical predictors for the toxicity of 102 patients treated with chemoradiotherapy followed by brachytherapy for LACC were used in this study. In addition to usual DVH parameters, 91 radiomic features were extracted from rectum, bladder and vaginal 3D dose distributions, after discretization into a fixed bin width of 1 Gy. They were evaluated for predictive modelling of rectal, genitourinary (GU) and vaginal toxicities (grade ≥ 2). Logistic Normal Tissue Complication Probability (NTCP) models were derived using clinical parameters only or combinations of clinical, DVH and radiomics. For rectal acute/late toxicities, the area under the curve (AUC) using clinical parameters was 0.53/0.65, which increased to 0.66/0.63, and 0.76/0.87, with the addition of DVH or radiomics parameters, respectively. For GU acute/late toxicities, the AUC increased from 0.55/0.56 (clinical only) to 0.84/0.90 (+DVH) and 0.83/0.96 (clinical + DVH + radiomics). For vaginal acute/late toxicities, the AUC increased from 0.51/0.57 (clinical only) to 0.58/0.72 (+DVH) and 0.82/0.89 (clinical + DVH + radiomics). The predictive performance of NTCP models based on radiomics features was higher than the commonly used clinical and DVH parameters. Dosimetric radiomics analysis is a promising tool for NTCP modelling in radiotherapy.

Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes.

Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.

Oregon Experiment with Medicaid Accountable Care and Pediatric Preventive Care Utilization Among Infants

Research ObjectiveIn August 2012, Oregon began enrolling Medicaid beneficiaries in coordinated care organizations (CCOs), a unique mandatory‐enrollment accountable care organization (ACO) model with payment methods strongly tied to preventive care and care coordination through patient‐centered medical homes. No study has examined the impact of CCOs on preventive care utilization among infants. The main goal of this study was to examine the impact of this new delivery model on the utilization of preventive care services among infants enrolled in Medicaid during 2 years after birth. We also investigated heterogeneous impacts of the CCO model for preterm and full‐term infants as well as over time.Study DesignWe linked Oregon 2008‐14 birth certificates to Medicaid eligibility and claims data, and created the pre‐ and post‐CCO cohorts of Medicaid infants. Medicaid infants were defined as being enrolled in Medicaid for at least 80% of the two years after birth. Pediatric preventive care outcomes included indicators of preventive care adequacy as recommended by American Academy of Pediatrics: six well‐child visits by the first birthday, nine well‐child visits by the second birthday, and annual developmental screening. We estimated logistic regression models and linear probability models, adjusting for time trends of outcomes and a comprehensive set of maternal and infant characteristics. We utilized a difference‐in‐differences approach to examine heterogeneous effects of CCOs for preterm and full‐term infants. Spline regression was used to test the effect of CCOs on the outcomes for every six months from the start of the CCO implementation. We conducted a range of sensitivity analyses, such as different thresholds of Medicaid enrollment for the definition of Medicaid infants and excluding infants of mothers in the Medicaid expansion group.Population Studied36 546 pre‐CCO infants born between August 2008 and July 2010 and 47 973 post‐CCO infants born between August 2012 and December 2014Principal FindingsDuring the 2 years after birth, infants enrolled in Medicaid CCOs had higher probability of well‐child visit adequacy: 10% points (95%CI: 8.2; 11.8) by the first birthday and 5.7% point (95%CI: 4.3; 7.1) by the second birthday. Annual probability of developmental screening increased by 23.1% points (95%CI: 21.6; 24.6) after the CCO implementation. No difference in the CCOs’ impact was found between preterm infants and full‐term infants. Increase in preventive care was found to start in the second year of the CCO implementation.ConclusionsThe CCO model was associated with an increase in preventive care utilization among infants enrolled in Medicaid during the first 2 years of birth.Implications for Policy or PracticeIncrease in preventive services among low‐income infants after CCO implementation implies that the Oregon Medicaid ACO model could improve quality of care for infants in their early years. A growing number of states have been adopting accountable care models for their Medicaid enrollees. Our findings suggest that other states benefit from studying the Oregon approach to accountable care for Medicaid enrollees that integrates financing and delivery of care, and implements a payment methodology with clear performance targets for pediatric preventive services.Primary Funding SourceCenters for Disease Control and Prevention.

Mobile Phone Use in a Car-Following Situation: Impact on Time Headway and Effectiveness of Driver's Rear-End Risk Compensation Behavior via a Driving Simulator Study.

Mobile phone use while driving has become one of the leading causes of traffic accidents and poses a significant threat to public health. This study investigated the impact of speech-based texting and handheld texting (two difficulty levels in each task) on car-following performance in terms of time headway and collision avoidance capability; and further examined the relationship between time headway increase strategy and the corresponding accident frequency. Fifty-three participants completed the car-following experiment in a driving simulator. A Generalized Estimating Equation method was applied to develop the linear regression model for time headway and the binary logistic regression model for accident probability. The results of the model for time headway indicated that drivers adopted compensation behavior to offset the increased workload by increasing their time headway by 0.41 and 0.59 s while conducting speech-based texting and handheld texting, respectively. The model results for the rear-end accident probability showed that the accident probability increased by 2.34 and 3.56 times, respectively, during the use of speech-based texting and handheld texting tasks. Additionally, the greater the deceleration of the lead vehicle, the higher the probability of a rear-end accident. Further, the relationship between time headway increase patterns and the corresponding accident frequencies showed that all drivers’ compensation behaviors were different, and only a few drivers increased their time headway by 60% or more, which could completely offset the increased accident risk associated with mobile phone distraction. The findings provide a theoretical reference for the formulation of traffic regulations related to mobile phone use, driver safety education programs, and road safety public awareness campaigns. Moreover, the developed accident risk models may contribute to the development of a driving safety warning system.

How skin color, class status, and gender intersect in the labor market: Evidence from a field experiment

This article examines skin color discrimination in two Brazilian labor markets using a field experimental approach. Fictitious resumes including photographs of job candidates were randomly assigned one skin color category via photo manipulation and submitted to entry-level job openings. In addition to assessing the extent of skin color discrimination, this article adopts an intersectional framework to examine how the effect of skin color in employment is moderated by class status and varies by gender. I found mixed results about the role of skin color in predicting the employment outcomes at the initial stages of the hiring process. Results from logistic regression and Linear Probability Models show that skin color is a weak predictor of hiring outcomes (e.g. receiving a callback from employers) among male applicants and for female applicants with brown skin. However, I find strong evidence that having dark skin is causally associated with hiring outcomes among female applicants. I also found that having a higher-class status erases skin color differences, thus identifying a potential mechanism that mitigates the effects of skin color in hiring.

The relationship between living arrangements and higher use of hospital care at middle and older ages: to what extent do observed and unobserved individual characteristics explain this association?

BackgroundPrevious research has shown that certain living arrangements, such as living alone, are associated with worse health at older ages. We assessed the association between living arrangements and hospital care use among middle-aged and older adults, and investigated to what extent observed and unobserved individual characteristics explain this association.MethodsLongitudinal Finnish registry data for men and women aged 50–89 years were used for the period 1987–2007. The relationship between living arrangements (based on whether an individual lived with a partner, other adults or alone, and whether they lived with minor/adult children) and heavy hospital care use (i.e., having been in hospital for 8 or more days in a year) was studied. First, we applied logistic regression models and linear probability models controlling for observed time-invariant factors (socioeconomic status measured by education, labour force status, and household income; and marital status), and then individual linear probability models with fixed-effects to further account for unobserved time-invariant individual characteristics in the measurement period. Analyses were done separately for 10 year age-groups.ResultsIn the logistic regression models, men and women who lived alone had higher crude odds of heavy hospital care use than those living only with their partner. These odds ratios were highest for men and women in the youngest age category (50–59 years, 1.72 and 1.36 respectively) and decreased with age. Adjusting for observed time-invariant socioeconomic status attenuated these odds by 14–40%, but adjusting for marital status did not affect the results. Lower odds were observed among adults aged 50–59 years who lived with their partner and (minor or adult) children. But odds were higher for individuals aged 60–79 years who co-resided with their adult children, regardless of whether they lived with a partner. Adjusting for observed time-invariant factors generally did not change these results. After further adjusting for unobserved time-invariant individual characteristics in the individual fixed-effects models, most of these associations largely attenuated or disappeared, particularly for ages 80–89 years.ConclusionsThe association between living arrangements and higher use of hospital care at middle and older ages is largely explained by socioeconomic disadvantage and unobserved time-invariant individual characteristics.

Impacto laboral de Wal-Mart en la productividad laboral. Un análisis por municipio en México

Si bien antes de los años 1990 ya había participación de grandes empresas minoristas en México, la llegada de empresas transnacionales, como Wal-Mart, generó una reconfiguración en la distribución de alimentos y, con ello, una expectativa del impacto directo sobre algunas variables del sector. Nuestro objetivo es determinar si la presencia de Wal-Mart implica una mayor productividad por trabajador. Para ello se construyó un modelo de probabilidad logística ordinal cuyos resultados a nivel municipal muestran una relación positiva y estadísticamente significativa, es decir, la probabilidad de encontrar un mayor nivel de productividad aumenta con una mayor presencia de dicha empresa.

Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles

ObjectivesWe sought to model chronic radiation-associated dysphagia (RAD) in patients given intensity-modulated radiation therapy (IMRT) for oropharyngeal squamous cell cancer (OPSCC) as a function of age and dose to non-target swallowing muscles. MethodsWe reviewed 300 patients with T1-T4 N0-3 M0 OPSCC given definitive IMRT with concurrent chemotherapy. Chronic RAD was defined as aspiration or stricture on videoflouroscopy/endoscopy, gastrostomy tube, or aspiration pneumonia at ≥12 months after IMRT. Doses to autosegmented regions of interest (ROIs; inferior, middle and superior constrictors, anterior and posterior digastrics, mylo/geniohyoid complex, intrinsic tongue, and gengioglossus) were obtained from DICOM-RT plans and dose-volume histograms. The probability of chronic RAD as a function of mean ROI dose, stratified by age (<50, 50–59, 60–69, or ≥70 years), was estimated with logistic probability models and subsequent unsupervised nonlinear curves. ResultsChronic RAD was observed in 34 patients (11%). Age was a significant correlate of chronic RAD, both independently and with dose for all muscle groups examined. Distinct muscle-specific dose–response profiles were observed as a function of age (e.g., 5% of patients in their 50 s [but 20% of those 70 + ] who received 60 Gy to the superior constrictor had chronic RAD). This effect was stable across all observed muscle ROIs, with a false discovery rate-corrected p < 0.05, for all dose/muscle/age models, suggesting that including age as a covariate improves modeling of chronic RAD. ConclusionsAge at treatment moderates the probability of chronic RAD after chemo-IMRT for OPSCC, with aging muscles showing lower dose thresholds. Uniform dose constraints may not predict toxicity in older patients.