Predictors of household direct cost of burn injury in adult patients at a tertiary healthcare facility in Ghana: an analytical cross-sectional study in Korle-Bu Teaching Hospital.

Introduction: treatment of severe burn injury generally requires enormous human and material resources including specialized intensive care, staged surgery, and continued restoration. This contributes to the enormous burden on patients and their families. The cost of burn treatment is influenced by many factors including the demographic and clinical characteristics of the patient. This study aimed to determine the costs of burn care and its associated predictive factors in Korle-Bu Teaching Hospital, Ghana. Methods: an analytical cross-sectional study was conducted among 65 consenting adult patients on admission at the Burns Centre of the Korle-Bu Teaching Hospital. Demographic and clinical characteristics of patients as well as the direct cost of burns treatment were obtained. Multiple regression analysis was done to determine the predictors of the direct cost of burn care. Results: a total of sixty-five (65) participants were enrolled in the study with a male-to-female ratio of 1.4: 1 and a mean age of 35.9 ± 14.6 years. Nearly 85% sustained between 10-30% total body surface area burns whilst only 6.2% (4) had burns more than 30% of total body surface area. The mean total cost of burns treatment was GHS 22,333.15 (USD 3,897.58). Surgical treatment, wound dressing and medication charges accounted for 45.6%, 27.5% and 9.8% of the total cost of burn respectively. Conclusion: the direct costs of burn treatment were substantially high and were predicted by the percentage of total body surface area burn and length of hospital stay.

Defining a screening tool for post-traumatic stress disorder in East Africa: a penalized regression approach.

Background: Scalable PTSD screening strategies must be brief, accurate and capable of administration by a non-specialized workforce. Methods: We used PTSD as determined by the structured clinical interview as our gold standard and considered predictors sets of (a) Posttraumatic Stress Checklist-5 (PCL-5), (b) Primary Care PTSD Screen for the DSM-5 (PC-PTSD) and, (c) PCL-5 and PC-PTSD questions to identify the optimal items for PTSD screening for public sector settings in Kenya. A logistic regression model using LASSO was fit by minimizing the average squared error in the validation data. Area under the receiver operating characteristic curve (AUROC) measured discrimination performance. Results: Penalized regression analysis suggested a screening tool that sums the Likert scale values of two PCL-5 questions-intrusive thoughts of the stressful experience (#1) and insomnia (#21). This had an AUROC of 0.85 (using hold-out test data) for predicting PTSD as evaluated by the MINI, which outperformed the PC-PTSD. The AUROC was similar in subgroups defined by age, sex, and number of categories of trauma experienced (all AUROCs>0.83) except those with no trauma history- AUROC was 0.78. Conclusion: In some East African settings, a 2-item PTSD screening tool may outperform longer screeners and is easily scaled by a non-specialist workforce.

Investigating the causal effects of COVID-19 vaccination on the adoption of protective behaviors in Japan: Insights from a fuzzy regression discontinuity design.

BACKGROUND: During the COVID-19 pandemic, concerns emerged that vaccinated individuals might engage less in infection-preventive behaviors, potentially contributing to virus transmission. This study evaluates the causal effects of COVID-19 vaccination on such behaviors within Japan, highlighting the significance of understanding behavioral dynamics in public health strategies. METHODS: Utilizing Japan's age-based vaccination priority for those born before April 1, 1957, this research employs a regression discontinuity design (RDD) to assess the vaccination's impact. Data from the fourth round of a longitudinal online survey, conducted from July 20 to 27, 2021, served as the basis for analyzing 14 infection-protective behaviors, including mask usage, handwashing, and avoiding crowds. RESULTS: A total of 12067 participants completed the survey. The analyzed sample size varied by outcome variable, ranging from 1499 to 5233. The analysis revealed no significant differences in the 14 behaviors examined among fully vaccinated, partially vaccinated, and unvaccinated individuals. This consistency across groups suggests that vaccination status did not significantly alter engagement in protective behaviors during the observation period. CONCLUSIONS: Empirical findings highlight the complexity of behavioral responses following vaccination, indicating that such responses may be influenced by various factors, rather than by vaccination status alone. Additionally, this result underscores the importance of crafting public health policies that account for the intricate interplay between vaccination and behavior. This study contributes to the broader discourse on managing responses to the pandemic and tailoring interventions to sustain or enhance protective health behaviors amid vaccination rollouts.

Worldwide incidence of suicides in prison: a systematic review with meta-regression analyses.

BACKGROUND: Suicide is a leading cause of death during imprisonment. This systematic review aimed to synthesise available evidence of prison suicide incidence worldwide. METHODS: We systematically searched the scientific literature, data repositories, and prison system reports, supplemented by correspondence with prison administrations. We included reports on people living in prison but excluded studies in preselected groups (by age or offence type). Absolute numbers and incidence rates of suicide mortality per 100 000 person-years by sex and country were extracted from 2000 to 2021. IQRs were used to describe the suicide incidence in different world regions. Incidence rate ratios comparing suicides of people living in prison with age-standardised general populations were calculated. We conducted meta-regression analyses on national-level and prison-level factors to examine heterogeneity. The study protocol was pre-registered with PROSPERO, CRD42021296819. FINDINGS: We included three scientific studies, 124 official reports, and 11 datasets from email correspondence. Between 2000 and 2021, there were 29 711 reported suicides during 91·2 million person-years of imprisonment in 82 jurisdictions worldwide (sex-specific data available for 13 289 individuals: 12 544 [94·4%] male and 745 [5·6%] female individuals). There were large variations between countries, with most studies reporting suicide rates in the range of 24-89 per 100 000 person-years in both sexes (22-86 in male individuals and 25-107 in female individuals). In meta-regression analyses, Europe (vs other regions), high-income countries (vs low-income and middle-income countries), and countries with lower incarceration rates (vs those with higher incarceration rates) had higher suicide rates. Incidence rate ratios between people who are incarcerated and age-standardised general populations in the same jurisdictions were typically in the range of 1·9-6·0 in male and 10·4-32·4 in female individuals. INTERPRETATION: Prison services worldwide, and particularly in Europe, should prioritise suicide prevention. Assessment and management of suicide risk in female individuals living in prison need particular attention due to excess mortality relative to community-based populations. Interpretation of synthesised data needs to be done with caution due to high heterogeneity between jurisdictions. FUNDING: Agencia Nacional de Investigación y Desarrollo, Economic and Social Research Council, and Wellcome Trust.

Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling.

Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learning-based QSAR models and provide better intuitive interpretation by extracting an approximate isometry between the chemical space of the drugs and their activity space.

The Garden of Forking Paths: Reinterpreting Haseman-Elston Regression for a Genotype-by-Environment Model.

Haseman-Elston regression (HE-reg) has been known as a classic tool for detecting an additive genetic variance component. However, in this study we find that HE-reg can capture GxE under certain conditions, so we derive and reinterpret the analytical solution of HE-reg. In the presence of GxE, it leads to a natural discrepancy between linkage and association results, the latter of which is not able to capture GxE if the environment is unknown. Considering linkage and association as symmetric designs, we investigate how the symmetry can and cannot hold in the absence and presence of GxE, and consequently we propose a pair of statistical tests, Symmetry Test I and Symmetry Test II, both of which can be tested using summary statistics. Test statistics, and their statistical power issues are also investigated for Symmetry Tests I and II. Increasing the number of sib pairs is important to improve statistical power for detecting GxE.

Penalized Regression Methods With Modified Cross-Validation and Bootstrap Tuning Produce Better Prediction Models.

Risk prediction models fitted using maximum likelihood estimation (MLE) are often overfitted resulting in predictions that are too extreme and a calibration slope (CS) less than 1. Penalized methods, such as Ridge and Lasso, have been suggested as a solution to this problem as they tend to shrink regression coefficients toward zero, resulting in predictions closer to the average. The amount of shrinkage is regulated by a tuning parameter, λ , $\lambda ,$ commonly selected via cross-validation ("standard tuning"). Though penalized methods have been found to improve calibration on average, they often over-shrink and exhibit large variability in the selected λ $\lambda $ and hence the CS. This is a problem, particularly for small sample sizes, but also when using sample sizes recommended to control overfitting. We consider whether these problems are partly due to selecting λ $\lambda $ using cross-validation with "training" datasets of reduced size compared to the original development sample, resulting in an over-estimation of λ $\lambda $ and, hence, excessive shrinkage. We propose a modified cross-validation tuning method ("modified tuning"), which estimates λ $\lambda $ from a pseudo-development dataset obtained via bootstrapping from the original dataset, albeit of larger size, such that the resulting cross-validation training datasets are of the same size as the original dataset. Modified tuning can be easily implemented in standard software and is closely related to bootstrap selection of the tuning parameter ("bootstrap tuning"). We evaluated modified and bootstrap tuning for Ridge and Lasso in simulated and real data using recommended sample sizes, and sizes slightly lower and higher. They substantially improved the selection of λ $\lambda $ , resulting in improved CS compared to the standard tuning method. They also improved predictions compared to MLE.

Adaptive design of experiments to fit surrogate Gaussian process regression models allows fast sensitivity analysis of the input waveform for patient-specific 3D CFD models of liver radioembolization.

BACKGROUND AND OBJECTIVE: Patient-specific 3D computational fluid dynamics (CFD) models are increasingly being used to understand and predict transarterial radioembolization procedures used for hepatocellular carcinoma treatment. While sensitivity analyses of these CFD models can help to determine the most impactful input parameters, such analyses are computationally costly. Therefore, we aim to use surrogate modelling to allow relatively cheap sensitivity analysis. As an example, we compute Sobol's sensitivity indices for three input waveform shape parameters. METHODS: We extracted three characteristic shape parameters from our input mass flow rate waveform (peak systolic mass flow rate, heart rate, systolic duration) and defined our 3D input parameter space by varying these parameters within 75 %-125 % of their nominal values. To fit our surrogate model with a minimal number of costly CFD simulations, we developed an adaptive design of experiments (ADOE) algorithm. The ADOE uses 100 Latin hypercube sampled points in 3D input space to define the initial design of experiments (DOE). Subsequently, we re-sample input space with 10,000 Latin Hypercube sampled points and cheaply estimate the outputs using the surrogate model. In each of 27 equivolume bins which divide our input space, we determine the most uncertain prediction of the 10,000 points, compute the true outputs using CFD, and add these points to the DOE. For each ADOE iteration, we calculate Sobol's sensitivity indices, and we continue to add batches of 27 samples to the DOE until the Sobol indices have stabilized. RESULTS: We tested our ADOE algorithm on the Ishigami function and showed that we can reliably obtain Sobol's indices with an absolute error <0.1. Applying ADOE to our waveform sensitivity problem, we found that the first-order sensitivity indices were 0.0550, 0.0191 and 0.407 for the peak systolic mass flow rate, heart rate, and the systolic duration, respectively. CONCLUSIONS: Although the current study was an illustrative case, the ADOE allows reliable sensitivity analysis with a limited number of complex model evaluations, and performs well even when the optimal DOE size is a priori unknown. This enables us to identify the highest-impact input parameters of our model, and other novel, costly models in the future.

[Trends in Oncomelania hupensis distribution in Wuhan City from 2003 to 2022 based on the Joinpoint regression model].

OBJECTIVE: To analyze the trends in Oncomelania hupensis distribution in Wuhan City, Hubei Province from 2003 to 2022, so as to provide insights into precision schistosomiasis control. METHODS: Data pertaining to O. hupensis snail survey in Wuhan City from 2003 to 2022 were collected. The trends in the proportion of areas with snail habitats, actual area with snail habitats, mean density of living snails and prevalence of Schistosoma japonicum infection in snails were evaluated in schistosomiasis-endemic areas of Wuhan City from 2003 to 2022 with the slope of trend curve (ß), annual percent change (APC) and average annual percent change (AAPC) using a Joinpoint regression model. RESULTS: During the period from 2003 through 2022, there were two turning points for the proportion of areas with snail habitats in Wuhan City in 2005 and 2015, with a rise during the period from 2003 to 2005 (ß1 = 5.93, t = 1.280, P > 0.05), a decline from 2005 to 2015 (ß2 = -0.88, t = -2.074, P > 0.05) and a rise from 2015 to 2022 (ß3 = 1.46, t = -2.356, P < 0.05). During the period from 2003 through 2022, there were two turning points for the proportion of areas with snail habitats in islet endemic areas of Wuhan City in 2006 and 2015, with no significant differences in the trends from 2003 to 2006 (ß1 = 4.64, t = 1.888, P > 0.05) or from 2006 to 2015 (ß2 = -1.45, t = -2.143, P > 0.05), and with a tendency towards a rise from 2015 to 2022 (ß3 = 2.04, t = -3.100, P < 0.05). During the period from 2003 through 2022, there were two turning points for the proportion of areas with snail habitats in inner embankment endemic areas of Wuhan City in 2012 and 2020, with a tendency towards a decline from 2003 to 2012 (ß1 = -0.39, t = -4.608, P < 0.05) and with no significant differences in the trends from 2012 to 2020 (ß2 = 0.03, t = 0.245, P > 0.05) and from 2020 to 2022 (ß3 = 1.38, t = 1.479, P > 0.05). During the period from 2003 to 2022, the actual area with snail habitats all appeared a tendency towards a decline in Wuhan City, and in islet and inner embankment endemic areas of Wuhan City from 2003 to 2022 (AAPC = -2.39%, -5.75% and -2.35%, all P values < 0.05). The mean density of living snails reduced from 0.087 snails/0.1 m2 in 2003 to 0.027 snails/0.1 m2 in 2022 in Wuhan City, with a significant difference in the tendency towards the decline (APC = AAPC = -11.47%, P < 0.05). The annual mean decline rate of the mean density of living snails was 17.36% in outside embankment endemic areas of Wuhan City from 2003 to 2022 (APC = AAPC = -17.36%, P < 0.05), and there was no significant difference in the trends in the mean density of living snails in islet endemic areas of Wuhan City from 2003 to 2022 (APC = AAPC = -0.97%, P > 0.05). In addition, the prevalence of S. japonicum infection in snails appeared a tendency towards a decline in Wuhan City from 2003 to 2022 (APC = AAPC = -12.45%, P < 0.05). CONCLUSIONS: The proportion of areas with snail habitats, actual area with snail habitats, mean density of living snails and prevalence of S. japonicum infection in snails all appeared a tendency towards a decline in Wuhan City from 2003 to 2022. Intensified snail control, modification of snail habitats, shrinking of areas with snails and implementation of grazing prohibition in snail-infested settings are required, in order to facilitate the progress towards schistosomiasis elimination in Wuhan City.

Iodine Status of Infants and Toddlers under 2 Years of Age and Its Association with Feeding Behaviors and Maternal Iodine Status in Shanghai: A Quantile Regression Analysis.

It is crucial to provide adequate iodine nutrition to infants and toddlers for proper thyroid function and subsequent brain development. Infants are particularly vulnerable to iodine deficiency during the transition from a milk-based diet (breast milk and/or infant formula) to solid food. This study examines the current iodine levels of children during their first two years of life and investigates the association between these levels and feeding behaviors and the iodine status of their mothers in Shanghai, a city located in eastern China. A hospital-based cohort study was conducted to enroll mother-child pairs, where the child is aged 6-23 months, who visited community health service centers in the 16 districts of Shanghai, China. Data on socio-demographic factors and feeding behavior data were collected from the participants. The urinary iodine concentration (UIC) in both the young children and their mothers were analyzed. A total of 2282 mother-child pairs were included in this analysis. The median (p25-p75) UIC for lactating women, weaning women, and children were 121.3 µg/L (68.1-206.4 µg/L), 123.4 µg/L (58.4-227.2 µg/L), and 152.1 µg/L (75.8-268.3 µg/L), respectively. The UIC in children was found to be higher than that in their mothers (p < 0.001). Children who consumed less than 500 mL per day of formula milk in the last week had lower UICs compared with those who consumed 500 mL per day or more (p = 0.026). Furthermore, the children's UIC was positively correlated with the maternal UIC (rs = 0.285, p < 0.001). Multiple quantile regression analysis revealed a statistically significant positive association between maternal UIC and children's UIC between the 0.1 and 0.9 quantiles (all p < 0.001). We found that the iodine status of infants and toddlers, as well as of mothers, was sufficient. However, a large minority of children and mothers may be at risk of iodine deficiency. Furthermore, no associations between children's UIC and feeding behaviors were observed. Moreover, there was a positive correlation between the UIC of young children and their mothers.

An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models.

The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.

Properties, quantile regression, and application of bounded exponentiated Weibull distribution to COVID-19 data of mortality and survival rates.

Well-known continuous distributions such as Beta and Kumaraswamy distribution are useful for modeling the datasets which are based on unit interval [0,1]. But every distribution is not always useful for all types of data sets, rather it depends on the shapes of data as well. In this research, a three-parameter new distribution named bounded exponentiated Weibull (BEW) distribution is defined to model the data set with the support of unit interval [0,1]. Some fundamental distributional properties for the BEW distribution have been investigated. For modeling dependence between measures in a dataset, a bivariate extension of the BEW distribution is developed, and graphical shapes for the bivariate BEW distribution have been shown. Several estimation methods have been discussed to estimate the parameters of the BEW distribution and to check the performance of the estimator, a Monte Carlo simulation study has been done. Afterward, the applications of the BEW distribution are illustrated using COVID-19 data sets. The proposed distribution shows a better fit than many well-known distributions. Lastly, a quantile regression model from bounded exponentiated Weibull distribution is developed, and its graphical shapes for the probability density function (PDF) and hazard function have been shown.

Cost-effectiveness of interventions for HIV/AIDS, malaria, syphilis, and tuberculosis in 128 countries: a meta-regression analysis.

BACKGROUND: Cost-effectiveness analyses have been conducted for many interventions for HIV/AIDS, malaria, syphilis, and tuberculosis, but they have not been conducted for all interventions that are currently recommended in all countries. To support national decision makers in the effective allocation of resources, we conducted a meta-regression analysis of published incremental cost-effectiveness ratios (ICERs) for interventions for these causes, and predicted ICERs for 14 recommended interventions for Global Fund-eligible countries. METHODS: In the meta-regression analysis, we used data from the Tufts University Center for the Evaluation of Value and Risk in Health (Boston, MA, USA) Cost-Effectiveness Registries (the CEA Registry beginning in 1976 and the Global Health CEA registry beginning in 1995) up to Jan 1, 2018. To create analysis files, we standardised and mapped the data, extracted additional data from published articles, and added variables from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Then we selected ratios for interventions with a minimum of two published articles and three published ICERs that mapped to one of five GBD causes (HIV/AIDS, malaria, syphilis, drug-susceptible tuberculosis, or multi-drug resistant tuberculosis), and to a GBD country; reported a currency year during or after 1990; and for which the comparator intervention was defined as no intervention, standard of care, or placebo. Our meta-regression analysis used all available data on 25 eligible interventions, and quantified the association between ICERs and factors at country level and intervention level. We used a five-stage statistical model that was developed to synthesise evidence on cost-effectiveness analyses, and we adapted it for smaller sample sizes by grouping interventions by cause and type (ie, prevention, diagnostics, and treatment). Using the meta-regression parameters we predicted country-specific median ICERs, IQRs, and 95% uncertainty intervals in 2019 US$ per disability-adjusted life-year (DALY) for 14 currently recommended interventions. We report ICERs in league tables with gross domestic product (GDP) per capita and country-specific thresholds. FINDINGS: The sample for the analysis was 1273 ratios from 144 articles, of which we included 612 ICERs from 106 articles in our meta-regression analysis. We predicted ICERs for antiretroviral therapy for prevention for two age groups and pregnant women, pre-exposure prophylaxis against HIV for two risk groups, four malaria prevention interventions, antenatal syphilis screening, two tuberculosis prevention interventions, the Xpert tuberculosis test, and chemotherapy for drug-sensitive tuberculosis. At the country level, ranking of interventions and number of interventions with a predicted median ICER below the country-specific threshold varied greatly. For instance, median ICERs for six of 14 interventions were below the country-specific threshold in Sudan, whereas 12 of 14 were below the country-specific threshold in Peru. Antenatal syphilis screening had the lowest median ICER among all 14 interventions in 81 (63%) of 128 countries, ranging from $3 (IQR 2-4) per DALY averted in Equatorial Guinea to $3473 (2244-5222) in Ukraine. Pre-exposure prophylaxis for HIV/AIDS for men who have sex with men had the highest median ICER among all interventions in 116 (91%) countries, ranging from $2326 (1077-4567) per DALY averted in Lesotho to $53 559 (23 841-108 534) in Maldives. INTERPRETATION: Country-specific league tables highlight the interventions that offer better value per DALY averted, and can support decision making at a country level that is more tailored to available resources than GDP per capita and country-specific thresholds. Meta-regression is a promising method to synthesise cost-effectiveness analysis results and transfer them across settings. FUNDING: Bill & Melinda Gates Foundation.

An empirical estimation of aggregate import demand under foreign exchange constraints: Evidence from Ethiopia.

This study employs the estimation of aggregate import demand under foreign exchange constraints in Ethiopia, utilizing annual time series data from 1985 to 2021. The regression analysis is conducted using the nonlinear autoregressive distributed lag (NARDL) approach to investigate the impact of the accumulation of foreign exchange reserves on aggregate import demand in Ethiopia. The estimation results indicate that, in the long run, all the variables, i.e., foreign exchange reserve, the relative price of imports, real income, volatility of the exchange rate, money supply, and policy dummy, significantly determine the behavior of aggregate imports over the reference period. The findings also show that, in the long run, foreign exchange reserve, real income, and the exchange rate positively affect the demand for imports in Ethiopia. Meanwhile, a positive shock in relative import price and money supply negatively affects import demand in Ethiopia. Thus, the price and income elasticity estimates have correct signs and are statistically significant. The variables included in the model strongly affect import demand in both the short and long run. Finally, policymakers aiming to significantly influence import demand should focus on effective management of these variables, as they strongly affect import volume.

Performance evaluation of different regression models: application in a breast cancer patient data.

This paper provides a comprehensive analysis of linear regression models, focusing on addressing multicollinearity challenges in breast cancer patient data. Linear regression methodologies, including GAM, Beta, GAM Beta, Ridge, and Beta Ridge, are compared using two statistical criteria. The study, conducted with R software, showcases the Beta regression model's exceptional performance, achieving a BIC of - 5520.416. Furthermore, the Ridge regression model demonstrates remarkable results with the best AIC at - 8002.647. The findings underscore the practical application of these models in real-world scenarios and emphasize the Beta regression model's superior ability to handle multicollinearity challenges. The preference for AIC over BIC in Generalized Additive Models (GAMs) is rooted in the AIC's calculation framework, highlighting its effectiveness in capturing the complexity and flexibility inherent in GAMs.

How far can air pollution affect tourism in China? Evidence from panel unconditional quantile regressions.

Previous studies provide empirical evidence for the connection between air pollution and tourism. However, many of them take the nexus as a linear one. It remains unexplored whether any thresholds are required for the nexus to materialize. This study systematically investigates whether PM2.5 concentrations-an essential indicator of air pollution-affect tourism in China at various tourism development levels. We analyze 284 Chinese cities from 2008 to 2018 using the Unconditional Quantile Regression method. Our statistical results reveal that air pollution positively influences tourism (regarding tourist visits and tourism revenue) in areas with low tourism development levels. However, a complex correlation between air pollution and tourism emerges when tourism development has reached a certain level. The correlation is initially negative, then positive, and finally disappears. But, the overall correlation remains negative. The effects of the interaction between air pollution and tourism resources on tourism are inverted U-shaped, implying that tourism resources can mitigate the negative effects of air pollution on tourism only when tourism development has reached a certain level. Based on the above findings, the associated policy implications are discussed.

Unrealistic expectations and disclosure of incurability in patients with non-small cell lung cancer.

PURPOSE: Determining whether patients' unrealistic expectations of chemotherapy as a cure were associated with their perception of the disclosure of incurability. METHODS: This prospective study included consecutive patients with pretreated non-small cell lung cancer from four study sites. Patients and their oncologists were asked whether they perceived the disclosure of cancer incurability. Patients were also asked if they thought that chemotherapy was curative. We followed up on whether the deceased patients received specialized palliative care 14 months after their last enrollment. Multiple regression analyses were conducted to examine the association between the expectation of chemotherapy as a cure and patient/oncologist-reported perceptions of the disclosure of incurability. RESULTS: We analyzed 200 patients, 77 (38.5%) of whom had unrealistic expectations of a cure. Based on patients' perceptions, incurability was disclosed to 138 (69.0%) patients, and based on their oncologists' perceptions, incurability was disclosed to 185 (92.5%) patients (patient/oncologist agreements, κ = 0.19). Patients without a perception of the oncologist's disclosure of incurability-regardless of their oncologist's perception-were more likely to have unrealistic expectations of a cure than patients for whom both patient and oncologist perceptions were present. Patients who had unrealistic expectations of chemotherapy as a cure were shown to be significantly less likely to have received specialized palliative care, after adjusting for covariates (adjusted OR, 0.45; 95% CI, 0.23-0.91; p = .027). CONCLUSION: Oncologists' disclosure of incurability was not fully recognized by patients, and expectations of chemotherapy as a cure were associated with patients' perception of the disclosure of incurability.

Heterogeneous peer effects of college roommates on academic performance.

Understanding how student peers influence learning outcomes is crucial for effective education management in complex social systems. The complexities of peer selection and evolving peer relationships, however, pose challenges for identifying peer effects using static observational data. Here we use both null-model and regression approaches to examine peer effects using longitudinal data from 5,272 undergraduates, where roommate assignments are plausibly random upon enrollment and roommate relationships persist until graduation. Specifically, we construct a roommate null model by randomly shuffling students among dorm rooms and introduce an assimilation metric to quantify similarities in roommate academic performance. We find significantly larger assimilation in actual data than in the roommate null model, suggesting roommate peer effects, whereby roommates have more similar performance than expected by chance alone. Moreover, assimilation exhibits an overall increasing trend over time, suggesting that peer effects become stronger the longer roommates live together. Our regression analysis further reveals the moderating role of peer heterogeneity. In particular, when roommates perform similarly, the positive relationship between a student's future performance and their roommates' average prior performance is more pronounced, and their ordinal rank in the dorm room has an independent effect. Our findings contribute to understanding the role of college roommates in influencing student academic performance.

Factors associated with changes in tacrolimus blood concentration after food initiation in patients with ulcerative colitis.

The therapeutic effect of tacrolimus against ulcerative colitis (UC) is correlated with its trough blood concentration. Conventionally, oral tacrolimus for the treatment of UC is initiated under fasting conditions; once the symptoms improve, food intake is resumed. Tacrolimus blood concentration decreases with food intake compared with that under fasting conditions. The aim of this study was to explore the characteristics of patients with UC whose tacrolimus blood concentrations tended to decrease after food initiation. Medical data of 13 patients with UC and treated with tacrolimus were retrospectively obtained. The participant characteristics associated with the changes in tacrolimus blood concentrations after food initiation were analyzed using regression analysis based on the rate of decrease in the concentration/dose (C/D) ratio after food initiation. Single regression analysis showed that the number of days required from tacrolimus initiation to food resumption (P = 0.0071) and individual differences in the increase in tacrolimus blood concentration after administration (P = 0.0247) were significantly associated with the rate of decrease in the C/D ratio after food initiation. Furthermore, multiple regression analysis showed a significant effect of the number of days to food resumption (P = 0.0004) and individual differences in the increase in tacrolimus blood concentration after administration (P = 0.0012). The results suggest that the degree of change in blood tacrolimus concentration after food initiation may be related to the severity of the symptoms and pathology of UC. Early identification of participant characteristics may help control tacrolimus blood concentration fluctuations after food initiation.