Examination of factors associated with the temporal stability assessment of crash severity by using generalised linear model-A case study.

Road crashes are a major public safety concern in Pakistan. Prior studies in Pakistan investigated the impact of different factors on road crashes but did not consider the temporal stability of crash data. This means that the recommendations based on these studies are not fully effective, as the impact of certain factors may change over time. To address this gap in the literature, this study aims to identify the factors contributing to crash severity in road crashes and examine how their impact varies over time. In this comprehensive study, we utilized Generalised Linear Model (GLM) on the crash data between the years 2013 to 2017, encompassing a total sample of 802 road crashes occurred on the N-5 road section in Pakistan, a 429-kilometer stretch connecting two big cities of Pakistan, i.e., Peshawar and Lahore. The purpose of the GLM was to quantify the temporal stability of the factors contributing crash severity in each year from 2013 to 2017. Within this dataset, 60% (n = 471) were fatal crashes, while the remaining 40% (n = 321) were non-fatal. The results revealed that the factors including the day of the week, the location of the crashes, weather conditions, causes of the crashes, and the types of vehicles involved, exhibited the temporal instability over time. In summary, our study provides in-depth insights aimed at reducing crash severity and potentially aiding in the development of effective crash mitigation policies in Pakistan and other nations having similar road safety problems. This research holds great promise in exploring the dynamic safety implications of emerging transportation technologies, particularly in the context of the widespread adoption of connected and autonomous vehicles.

The entropy rate of Linear Additive Markov Processes.

This work derives a theoretical value for the entropy of a Linear Additive Markov Process (LAMP), an expressive but simple model able to generate sequences with a given autocorrelation structure. Our research establishes that the theoretical entropy rate of a LAMP model is equivalent to the theoretical entropy rate of the underlying first-order Markov Chain. The LAMP model captures complex relationships and long-range dependencies in data with similar expressibility to a higher-order Markov process. While a higher-order Markov process has a polynomial parameter space, a LAMP model is characterised only by a probability distribution and the transition matrix of an underlying first-order Markov Chain. This surprising result can be explained by the information balance between the additional structure imposed by the next state distribution of the LAMP model, and the additional randomness of each new transition. Understanding the entropy of the LAMP model provides a tool to model complex dependencies in data while retaining useful theoretical results. To emphasise the practical applications, we use the LAMP model to estimate the entropy rate of the LastFM, BrightKite, Wikispeedia and Reuters-21578 datasets. We compare estimates calculated using frequency probability estimates, a first-order Markov model and the LAMP model, also considering two approaches to ensure the transition matrix is irreducible. In most cases the LAMP entropy rates are lower than those of the alternatives, suggesting that LAMP model is better at accommodating structural dependencies in the processes, achieving a more accurate estimate of the true entropy.

Assessment of the E-value in the presence of bias amplification: a simulation study.

BACKGROUND: The E-value, a measure that has received recent attention in the comparative effectiveness literature, reports the minimum strength of association between an unmeasured confounder and the treatment and outcome that would explain away the estimated treatment effect. This study contributes to the literature on the applications and interpretations of E-values by examining how the E-value is impacted by data with varying levels of association of unobserved covariates with the treatment and outcome measure when covariate adjustment is applied. We calculate the E-value after using regression and propensity score methods (PSMs) to adjust for differences in observed covariates. Propensity score methods are a common observational research method used to balance observed covariates between treatment groups. In practice, researchers may assume propensity score methods that balance treatment groups across observed characteristics will extend to balance of unobserved characteristics. However, that assumption is not testable and has been shown to not hold in realistic data settings. We assess the E-value when covariate adjustment affects the imbalance in unobserved covariates. METHODS: Our study uses Monte Carlo simulations to evaluate the impact of unobserved confounders on the treatment effect estimates and to evaluate the performance of the E-Value sensitivity test with the application of regression and propensity score methods under varying levels of unobserved confounding. Specifically, we compare observed and unobserved confounder balance, odds ratios of treatment vs. control, and E-Value sensitivity test statistics from generalized linear model (GLM) regression models, inverse-probability weighted models, and propensity score matching models, over correlations of increasing strength between observed and unobserved confounders. RESULTS: We confirm previous findings that propensity score methods - matching or weighting - may increase the imbalance in unobserved confounders. The magnitude of the effect depends on the strength of correlation between the confounder, treatment, and outcomes. We find that E-values calculated after applying propensity score methods tend to be larger when unobserved confounders result in more biased treatment effect estimates. CONCLUSIONS: The E-Value may misrepresent the size of the unobserved effect needed to change the magnitude of the association between treatment and outcome when propensity score methods are used. Thus, caution is warranted when interpreting the E-Value in the context of propensity score methods.

Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects.

Modern biomedical datasets are increasingly high-dimensional and exhibit complex correlation structures. Generalized linear mixed models (GLMMs) have long been employed to account for such dependencies. However, proper specification of the fixed and random effects in GLMMs is increasingly difficult in high dimensions, and computational complexity grows with increasing dimension of the random effects. We present a novel reformulation of the GLMM using a factor model decomposition of the random effects, enabling scalable computation of GLMMs in high dimensions by reducing the latent space from a large number of random effects to a smaller set of latent factors. We also extend our prior work to estimate model parameters using a modified Monte Carlo Expectation Conditional Minimization algorithm, allowing us to perform variable selection on both the fixed and random effects simultaneously. We show through simulation that through this factor model decomposition, our method can fit high-dimensional penalized GLMMs faster than comparable methods and more easily scale to larger dimensions not previously seen in existing approaches.

The Role of an Early Childhood Community Health Worker in Addressing Psycho-Social Needs in the Perinatal and Early Childhood Period.

OBJECTIVE: Addressing family psychosocial and mental health needs in the perinatal and early childhood period has a significant impact on long-term maternal and child health and is key to achieving health equity. We aimed to (1) describe and evaluate the role of an Early Childhood Community Health Worker (EC-CHW) to address psychosocial needs and improve psychosocial well-being for families in the perinatal period, and (2) examine factors associated with completion of goals. METHODS: An EC-CHW program was modeled after an existing hospital CHW program for children with special healthcare needs and chronic disease. An evaluation was conducted using repeated measures to assess improvements in psychosocial outcomes such as family stress and protective factors after participating in the EC-CHW program. Linear regression was also used to assess factors associated with completion of goals. RESULTS: Over a 21-month period (January 2019-September 2020), 161 families were referred to the EC-CHW. The most common reasons for referral included social needs and navigating systems for child developmental and behavioral concerns. There were high rates of family engagement in services (87%). After 6 months, families demonstrated statistically significant improvements in protective factors including positive parenting knowledge and social support. Only 1 key predictor variable, maternal depression, showed significant associations with completion of goals in the multivariable analysis. CONCLUSIONS: This study demonstrated the need for, and potential impact of an EC-CHW in addressing psychosocial and mental health needs in the perinatal period, and in a primary care setting. Impacts on protective factors are promising.

Racial iniquity in mortality from cervical cancer in Brazil: a time trend study from 2002 to 2021. / Iniquidade racial na mortalidade por câncer de colo de útero no Brasil: estudo de séries temporais de 2002 a 2021.

This ecological study examined time series, from 2002 to 20121, of age-adjusted coefficients of cervical cancer mortality, in Brazil, in women aged 20 years or more, by race. The information sources were Brazil's mortality information system (Sistema de Informação sobre Mortalidade - SIM) and the official bureau of statistics (Instituto Brasileiro de Geografia e Estatística - IBGE). Annual changes in age-adjusted mortality rates were calculated using the Prais-Winsten linear regression method. Black women die more and the rate is decreasing less. Racial inequality has increased over the years. In 2002, there were 0.08 more deaths per 100,000 women in the black population than among white women; in 2021, the number was one death. Health policymaking should consider racial differences in the implementation of strategies and goals.

O objetivo desse artigo é analisar séries temporais da mortalidade por câncer de colo do útero segundo raça/cor no Brasil de 2002 a 2021. Estudo ecológico de séries temporais com dados do Sistema de Informação sobre Mortalidade e informações populacionais do IBGE. Variações anuais das taxas de mortalidade ajustadas por idade de mulheres de 20 anos ou mais foram estimadas pelo modelo de regressão linear simples com correção de Prais-Winsten. Foram registrados 133.429 óbitos por câncer de colo de útero, destes, 51,2% foram de mulheres negras. As mulheres negras morrem mais e têm menor queda do coeficiente. Houve aumento da desigualdade racial ao longo dos anos. Em 2002, ocorriam 0,08 óbitos/100 mil mulheres a mais na população negra comparada com a população branca; em 2021 esse número é de aproximadamente 1 óbito. Para a elaboração de políticas de saúde da mulher devem ser consideradas as diferenças raciais na implementação de estratégias e metas.

Treatable mortality and health care related factors across European countries.

Introduction: Despite the improvements in European health systems, a large number of premature deaths are attributable to treatable mortality. Men make up the majority of these deaths, with a significant gap existing between women and men's treatable mortality rate in the EU. Aim: This study aims to identify the healthcare-related factors, including health expenditures, human and physical resources, and hospital services use associated with treatable mortality in women and men across European countries during the period 2011-2019. Methods: We use Eurostat data for 28 EU countries in the period 2011-2019. We estimate a panel data linear regression with country fixed effects and quantile linear regression for men and women. Results: The results found (i) differences in drivers for male and female treatable mortality, but common drivers hold the same direction for both sexes; (ii) favorable drivers are GDP per capita, health expenditures, number of physicians per capita, and (only for men) the average length of a hospital stay, (iii) unfavorable drivers are nurses and beds per capita, although nurses are not significant for explaining female mortality. Conclusion: Policy recommendations may arise that involve an improvement in hospital bed management and the design of more specific policies aimed at healthcare professionals.

Assessment of flusilazole residues on apples: Detection, quantification and health risk implications.

Apple, a major fruit of temperate Himalayas, is sprayed with chemical pesticides around 12 times during the cropping season. Various systemic and contact fungicides are applied for the management of major diseases. In order to manage disease, flusilazole 40 EC is frequently used. However, excessive chemical application has been found to be detrimental for consumer safety. Keeping in view consumer safety, risk assessment, the half-life and waiting period for flusilazole 40 EC were evaluated on the Red Velox variety of apple. The QuEChERS (quick, easy, cheap, effective, rugged and safe) method and high-performance liquid chromatography were adapted for sample processing and analysis, respectively. The recovery percentages of flusilazole at three fortification levels (0.04, 0.09 and 0.50 mg kg-1) were 98.85, 99.83 and 98.98%, respectively. Flusilazole at the recommended dose (80 g a.i. ha-1) left an initial deposit of 0.733 mg kg-1, which dissipated by 93.45% in 60 days and was non-detectable beyond this period. Meanwhile flusilazole at double the recommended dose (160 g a.i. ha-1) left an initial deposit of 0.913 mg kg-1, which dissipated by 93.43% in 70 days and was non-detectable beyond this period. Based on the maximum residue limit of 0.3 mg kg-1 as prescribed by the Codex Alimentarius Commission, a waiting period of 28.74 and 46.03 days was recorded for single and double doses, respectively. Moreover, in order to assess the consumer risk, theoretical maximum residue contributions (TMRCs) were derived using flusilazole residues (average and maximum) recorded at various time intervals and compared with the maximum permissible intake, which was found to be 0.42 mg per person per day. Based on the average per capita daily consumption of 6.76 g apple in India, the TMRC values were computed. Although the values of TMRC decreased below maximum permissible intake at the first day after application, indicating minimal consumer health risks, fruits sprayed with a double dose of flusilazole carried the risk even up to the tenth day after flusilazole application. The results of the present study will be valuable for safe and timely use of flusilazole on apple.

Bioavailability assessment of a brompheniramine taste-masked pediatric formulation in a juvenile porcine model.

A brompheniramine taste-masked pediatric formulation was developed as part of the National Institutes of Health Pediatric Formulation Initiative to help address low patient compliance caused by the bitter taste of many adult formulations. To confirm that the taste-masked formulation can provide a similar pharmacological effect to the previous marketed adult formulations, a juvenile porcine model was used to screen the model pediatric formulation to compare the bioavailability between the marketed brompheniramine maleate and the taste-masked maleate/tannate formulation. Pigs were dosed orally with both formulations and blood samples were obtained from 0 to 48 h. Plasma samples were prepared and extracted using solid-phase extraction. The mass spectrometer was operated under selected ion monitoring mode. The selected ion monitoring channels were set to m/z 319.1 for brompheniramine and m/z 275.2 for the internal standard chlorpheniramine. Calibration curves were linear over the analytical range 0.2-20 ng/ml (r2 > 0.995) for brompheniramine in plasma. The intra- and inter-day accuracies were between 98.0 and 105% with 5.73% RSD precision. The bioanalytical method was successfully applied to a preclinical bioavailability study. The bioavailability profiles were not significantly different between the two formulations, which demonstrates that taste-masking with tannic acid is a promising approach for formulation modification for pediatric patients.

Simplified Methods for Modelling Dependent Parameters in Health Economic Evaluations: A Tutorial.

BACKGROUND: In health economic evaluations, model parameters are often dependent on other model parameters. Although methods exist to simulate multivariate normal (MVN) distribution data and estimate transition probabilities in Markov models while considering competing risks, they are technically challenging for health economic modellers to implement. This tutorial introduces easily implementable applications for handling dependent parameters in modelling. METHODS: Analytical proofs and proposed simplified methods for handling dependent parameters in typical health economic modelling scenarios are provided, and implementation of these methods are illustrated in seven examples along with the SAS and R code. RESULTS: Methods to quantify the covariance and correlation coefficients of correlated variables based on published summary statistics and generation of MVN distribution data are demonstrated using examples of physician visits data and cost component data. The use of univariate normal distribution data instead of MVN distribution data to capture population heterogeneity is illustrated based on the results from multiple regression models with linear predictors, and two examples are provided (linear fixed-effects model and Cox proportional hazards model). A conditional probability method is introduced to handle two or more state transitions in a single Markov model cycle and applied in examples of one- and two-way state transitions. CONCLUSIONS: This tutorial proposes an extension of routinely used methods along with several examples. These simplified methods may be easily applied by health economic modellers with varied statistical backgrounds.

Migration-related determinants of health-related quality of life of persons with direct migration background in Germany: a study based on the German Socio-Economic Panel.

Introduction: Persons with a direct migration background made up a share of around 17% of the total German population in the year 2020. Not much is known about migration-related determinants of health-related quality of life (HrQoL) of persons with direct migration background. This study aimed to analyze the associations between HrQoL, sociodemographic, and migration-related characteristics of persons with direct migration background in Germany. Methods: The sample of this study was based on four waves (2014, 2016, 2018, and 2020) of the migration samples (M1 and M2) of the German Socio-Economic Panel (SOEP). The SF-12 was used to measure HrQoL using its mental (MCS) and physical (PCS) component summary scores. Missing information was replaced by multiple imputation by chained equations with predictive mean matching. Associations between HrQoL and sociodemographic and migration-related characteristics were examined using multilevel mixed-effects linear regressions. Results: The mean MCS and PCS scores of persons with direct migration background(n = 4,124) were 51.81 and 51.57, respectively. Being born in Russia was associated statistically significantly with a lower PCS score compared with non-east European and American/Oceanic countries. A longer period since migration to Germany was negatively associated with both MCS and PCS scores (both with p < 0.01). A steady relationship before migration was associated with a higher MCS score (+0.69, p = 0.017). Not feeling German and experiencing disadvantages due to origin were negatively associated with the MCS (both with p < 0.001). The oral ability in the German language was positively associated with the PCS score (p < 0.05). Conclusion: The time since migration to Germany, and the relationship status before migration may be determinants of HrQoL of persons with a direct migration background. Furthermore, connectedness with Germany, disadvantages due to origin and oral ability in the German language, representative of integration in Germany, can be potential determinants of HrQoL. Thus, integration of persons with migration background is crucial for their mental and physical HrQoL.

The long and short of it: a comprehensive assessment of axial length estimation in myopic eyes from ocular and demographic variables.

BACKGROUND/OBJECTIVES: Axial length, a key measurement in myopia management, is not accessible in many settings. We aimed to develop and assess machine learning models to estimate the axial length of young myopic eyes. SUBJECTS/METHODS: Linear regression, symbolic regression, gradient boosting and multilayer perceptron models were developed using age, sex, cycloplegic spherical equivalent refraction (SER) and corneal curvature. Training data were from 8135 (28% myopic) children and adolescents from Ireland, Northern Ireland and China. Model performance was tested on an additional 300 myopic individuals using traditional metrics alongside the estimated axial length vs age relationship. Linear regression and receiver operator characteristics (ROC) curves were used for statistical analysis. The contribution of the effective crystalline lens power to error in axial length estimation was calculated to define the latter's physiological limits. RESULTS: Axial length estimation models were applicable across all testing regions (p ≥ 0.96 for training by testing region interaction). The linear regression model performed best based on agreement metrics (mean absolute error [MAE] = 0.31 mm, coefficient of repeatability = 0.79 mm) and a smooth, monotonic estimated axial length vs age relationship. This model was better at identifying high-risk eyes (axial length >98th centile) than SER alone (area under the curve 0.89 vs 0.79, respectively). Without knowing lens power, the calculated limits of axial length estimation were 0.30 mm for MAE and 0.75 mm for coefficient of repeatability. CONCLUSIONS: In myopic eyes, we demonstrated superior axial length estimation with a linear regression model utilising age, sex and refractive metrics and showed its clinical utility as a risk stratification tool.

Sociodemographic disparities in sedentary time among US youth vary by period of the day.

INTRODUCTION: Studies have reported sex and race/ethnicity disparities in sedentary time (ST), but none have evaluated ST by well-defined periods of the weekday (before school, during school, afterschool, and evening) and weekend day (morning, afternoon, and evening). Comparing sex and race/ethnicity disparities in ST at different periods of a weekday and weekend day can deepen our understanding of disparities and inform intervention efforts. This study tests sex and race/ethnicity disparities in ST by period of day in a representative sample of US youth. METHODS: Youth (N = 2,972) from the 2003-2006 NHANES waves reported demographic variables and wore an accelerometer for 7 consecutive days to assess ST. Linear regressions were conducted to test relationships between sex and race/ethnicity and ST (min/hour) during each period of a weekday and weekend day. ST differences by sex and race/ethnicity were calculated to identify the periods of the day presenting the largest opportunity to reduce disparities. RESULTS: Females were more sedentary than males during school (p < 0∙0001), afterschool (p < 0∙0001), and weekday evenings (p < 0∙0001) after controlling for covariates. After controlling for covariates, race/ethnicity only was a significant predictor of ST during weekend mornings (p < 0∙0001). During school and afterschool emerged as the periods with the largest opportunities to reduce sex disparities in ST. Weekend mornings were identified as the largest opportunity to reduce race/ethnic disparities in ST. CONCLUSIONS: Sex disparities in ST appear to be driven mostly by the during school period of the day, while race/ethnic disparities in ST seem to be driven by the weekend morning period. Future intervention work should consider these periods when aiming to reduce ST disparities in youth.

Assessment of linear regression of peripapillary optical coherence tomography retinal nerve fiber layer measurements to forecast glacuoma trajectory.

Linear regression of optical coherence tomography measurements of peripapillary retinal nerve fiber layer thickness is often used to detect glaucoma progression and forecast future disease course. However, current measurement frequencies suggest that clinicians often apply linear regression to a relatively small number of measurements (e.g., less than a handful). In this study, we estimate the accuracy of linear regression in predicting the next reliable measurement of average retinal nerve fiber layer thickness using Zeiss Cirrus optical coherence tomography measurements of average retinal nerve fiber layer thickness from a sample of 6,471 eyes with glaucoma or glaucoma-suspect status. Linear regression is compared to two null models: no glaucoma worsening, and worsening due to aging. Linear regression on the first M ≥ 2 measurements was significantly worse at predicting a reliable M+1st measurement for 2 ≤ M ≤ 6. This range was reduced to 2 ≤ M ≤ 5 when retinal nerve fiber layer thickness measurements were first "corrected" for scan quality. Simulations based on measurement frequencies in our sample-on average 393 ± 190 days between consecutive measurements-show that linear regression outperforms both null models when M ≥ 5 and the goal is to forecast moderate (75th percentile) worsening, and when M ≥ 3 for rapid (90th percentile) worsening. If linear regression is used to assess disease trajectory with a small number of measurements over short time periods (e.g., 1-2 years), as is often the case in clinical practice, the number of optical coherence tomography examinations needs to be increased.

A stepped-wedge randomised controlled trial to assess efficacy and cost-effectiveness of a care-bundle to prevent falls in older hospitalised patients.

BACKGROUND: Patient accidental falls in a hospital environment are a serious problem for patient safety, and for the additional costs due to associated medical interventions. OBJECTIVE: The endpoints of this study were the assessment of the fall incidence in the hospital before and after the implementation of a multidisciplinary care-bundle, along with a cost-effectiveness evaluation. DESIGN: A stepped-wedge trial was conducted between April 2015 and December 2016 in Bologna University Hospital. METHODS: Incidence rates (IRs) of falls in both the control and intervention periods were calculated. A multilevel mixed-effects generalised linear model with logit link function, adjusted for age, sex, cluster cross-over timing and patients' clinical severity was used to estimate odds ratios (OR) of fall risk of patients of the intervention group respect to the controls.Intervention costs associated with the introduction of the care-bundle intervention were spread between patients per cluster-period-group of exposure. Incremental cost-effectiveness ratio was evaluated using total costs in the intervention and control groups. RESULTS: IRs of falls in control and intervention periods were respectively 3.15 and 2.58 for 1,000 bed-days. After adjustment, the subjects receiving the intervention had a statistically significant reduced risk of falling with respect to those who did not (OR = 0.71, 95% confidence interval: 0.60-0.84). According to the cost-effectiveness analysis, the incremental cost per fall prevented was €873.92 considering all costs, and €1644.45 excluding costs related falls. CONCLUSIONS: Care-bundle had a protective effect on patients, with a statistically significant reduction of the fall risk. This type of intervention appears cost-effective compared to routine practices.

How Hermann J. Muller Viewed the Ernest Sternglass Contributions to Hereditary and Cancer Risk Assessment.

ABSTRACT: As one of the most influential radiation geneticists of the 20th century, Hermann J. Muller had a major role in the development and widespread acceptance of the linear no-threshold (LNT) dose response for hereditary and cancer risk assessments worldwide. However, a spate of historical reassessments have challenged the fundamental scientific foundations of the LNT model, drawing considerable attention to issues of ethical probity and the scientific leadership of Muller. This review paper raises further questions about the objectivity of Muller with respect to the LNT model. It is shown that Muller supported Ernest Sternglass's findings and interpretations concerning radiation-induced childhood leukemia, which have been widely and consistently discredited. These findings provide further evidence that Muller's actions with respect to radiation cancer risk assessment were far more ideologically than scientifically based.

The cost of doubt: assessing the association between attributional ambiguity and mental health.

OBJECTIVE: To quantify the association between attributional ambiguity-the uncertainty of whether an experience is discrimination-and mental health. METHODS: Using a nationally representative sample of U.S. adults recruited through an online survey by Ipsos (April 23 and May 3, 2021), attributional ambiguity was quantified by asking participants if they experienced anything in the past 6 months that they were unsure was discrimination. The survey also assessed the degree to which these experiences caused participants to feel bothered and to ruminate on them. Multiple linear regression models were used to analyze associations between attributional ambiguity and depressive symptoms and mental health status. RESULTS: Black and Hispanic participants reported higher rates of attributional ambiguity than White participants. Experiencing attributional ambiguity was associated with higher levels of depressive symptoms and poorer self-reported mental health status. Among those who reported attributional ambiguity, increases in bother and rumination scores were positively associated with depressive symptoms. CONCLUSION: Attributional ambiguity is an important yet overlooked social determinant of mental health. More research is needed to fully understand the impact of this stressor on population health, particularly among minoritized populations.

How provider payment methods affect health expenditure of depressive patients? Empirical study from national claims data in China from 2013 to 2017.

BACKGROUND: This study aimed to investigate the associations between provider payment methods and expenditure of depressive patients, stratified by service types and hospital levels. METHODS: We used a 5 % random sample of urban claims data in China (2013-2017), collected by China Health Insurance Research Association. Provider payment methods (fee-for-services, global budget, capitation, case-based and per-diem payments) were the explanatory variables. A generalized linear model was fitted for the associations between provider payment methods and expenditure. All analyses were adjusted for patient"cioeconomic and health-related characteristics. RESULTS: In total, 64,615 depressive patient visits were included, 59,459 for outpatients and 5156 for inpatients. Female patients accounted for 63.00 %. The total and out-of-pocket (OOP) expenditure significantly differentiated by provider payments. Among outpatient services, when comparing with fee-for-services, capitation payment was associated with substantial marginal reduction in total and OOP expenditure (-$34.18, -$9.71) in primary institutes, yet increases ($27.26, $24.11) in secondary hospitals. Similarly, global budget was associated with lower total and OOP expenditure (-$13.51, -$1.61) in secondary hospitals, while higher total and OOP expenditure ($7.43, $32.27) in tertiary hospitals than fee-for-services. For inpatients, total and OOP expenditures under per-diem (-$857.65, -$283.48) and case-based payments (-$997.93, -$137.56) were remarkably smaller than those under fee-for-services in primary and secondary hospitals, respectively. Besides, case-base payment was only linked with the largest reduction in OOP expense (-$239.39) in inpatient services of tertiary hospitals. LIMITATION: Only urban claims data was included in this study, and investigations for rural population still warrant. And updated data are needed for future studies. CONCLUSIONS: There were varying correlations between provider payment methods and expenditure, which differed by service types and hospital levels. These findings provided empirical evidence for optimizing the mixed payment methods for depression in China.

Go for Economic Transformation and Development in China: Financial Development, Higher Education, and Green Technology Evolution.

Technology innovation is the key driving force in achieving economic transformation and development. Financial development and the expansion of higher education can promote technological progress primarily by easing financing constraints and improving the level of human capital. This study examines the impact of financial development and higher education expansion on green technology innovation. It conducts an empirical analysis by constructing a linear panel model and a nonlinear threshold model. The present study sample is based on the urban panel data of China from 2003-2019. (1) Financial development can significantly promote the expansion of higher education. (2) The expansion of higher education can improve energy and environment-based technological progress. (3) Financial development can both directly and indirectly promote green technology evolution by expanding higher education. The joint financial development and higher education expansion can significantly empower green technology innovation. (4) In the process of promoting green technology innovation, financial development has a non-linear influence on it, with higher education as the threshold. The effect of financial development on green technology innovation varies according to the degree of higher education. Based on these findings, we put forward policy proposals for green technology innovation to promote economic transformation and development in China.

Estimating Costs Associated with Disease Model States Using Generalized Linear Models: A Tutorial.

Estimates of costs associated with disease states are required to inform decision analytic disease models to evaluate interventions that modify disease trajectory. Increasingly, decision analytic models are developed using patient-level data with a focus on heterogeneity between patients, and there is a demand for costs informing such models to reflect individual patient costs. Statistical models of health care costs need to recognize the specific features of costs data which typically include a large number of zero observations for non-users, and a skewed and heavy right-hand tailed distribution due to a small number of heavy healthcare users. Different methods are available for modelling costs, such as generalized linear models (GLMs), extended estimating equations and latent class approaches. While there are tutorials addressing approaches to decision modelling, there is no practical guidance on the cost estimation to inform such models. Therefore, this tutorial aims to provide a general guidance on estimating healthcare costs associated with disease states in decision analytic models. Specifically, we present a step-by-step guide to how individual participant data can be used to estimate costs over discrete periods for participants with particular characteristics, based on the GLM framework. We focus on the practical aspects of cost modelling from the conceptualization of the research question to the derivation of costs for an individual in particular disease states. We provide a practical example with step-by-step R code illustrating the process of modelling the hospital costs associated with disease states for a cardiovascular disease model.