Upgrading Model Selection Criteria with Goodness of Fit Tests for Practical Applications

Influence of Age, Gender and Body Mass Index on Intravenous Pharmacokinetics of Buprenorphine in Children Undergoing Orthopedic Surgery

Background

Buprenorphine (BPN) is a widely used analgesic in the pediatric population, although there are few studies on the pharmacokinetics and pharmacodynamics of this drug.

Objective

The objective was to characterize the pharmacokinetics of BPN after intravenous administration and analyze the effect of age, gender, weight, height, body mass index (BMI), and drug-drug interactions as covariates.

Methods

Ninety-nine children (2-10 years), who underwent orthopedic surgery under regional, general, or combined anesthesia were included. Patients evaluated according to the American Society of Anesthesiologists Physical Status Classification, who received intravenous BPN 2 μg/kg were enrolled. Blood was collected from 1-240 min. Drug plasma concentrations were determined by LC-MS/MS. Population pharmacokinetic parameters were obtained with Monolix 2021R1 software. Pearson's correlation and/or ANOVA were used for statistical analysis.

Results

Age was associated with changes in clearance and central compartment volume and the female gender was associated with lower intercompartmental clearance, while BMI modified clearance, central and peripheral compartment volume. Concomitant administration of BPN with fentanyl and dexamethasone produced decreases in clearance.

Conclusions

The covariates of sex, age, and BMI are directly related to the increase or decrease in BPN pharmacokinetic parameters.

A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis

BACKGROUND

Patient heterogeneity poses significant challenges for managing individuals and designing clinical trials, especially in complex diseases. Existing classifications rely on outcome-predicting scores, potentially overlooking crucial elements contributing to heterogeneity without necessarily impacting prognosis.

METHODS

To address patient heterogeneity, we developed ClustALL, a computational pipeline that simultaneously faces diverse clinical data challenges like mixed types, missing values, and collinearity. ClustALL enables the unsupervised identification of patient stratifications while filtering for stratifications that are robust against minor variations in the population (population-based) and against limited adjustments in the algorithm's parameters (parameter-based).

RESULTS

Applied to a European cohort of patients with acutely decompensated cirrhosis (n = 766), ClustALL identified five robust stratifications, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features; notably, the 3-cluster stratification showed a prognostic value. Re-assessment of patient stratification during follow-up delineated patients' outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n = 580).

CONCLUSIONS

By applying ClustALL to patients with acutely decompensated cirrhosis, we identified three patient clusters. Following these clusters over time offers insights that could guide future clinical trial design. ClustALL is a novel and robust stratification method capable of addressing the multiple challenges of patient stratification in most complex diseases.

Evaluating the impact of PD-1 inhibitor treatment on key health outcomes for cancer patients in China

BACKGROUND

There is a lack of studies examining the influence of programmed cell death protein 1 (PD-1) inhibitors on the health outcomes of cancer patients in China.

AIM

This study aimed to evaluate prospective health outcomes associated with introducing PD-1 inhibitor treatment in China over five years.

METHOD

We constructed a partitioned survival model to assess disparities in health outcomes over a 5-year time frame between two scenarios: one involving the availability of PD-1 inhibitor class with standard of care and the other involving standard of care alone. The impact on various health outcomes were assessed, including life years (LYs) gained, quality-adjusted life years (QALYs) gained, progression-free survival (PFS) years gained, the reduction in the number of grade 3-5 adverse events (AEs), and the improvement in objective remission rates (ORR). A sensitivity analysis was conducted to assess the robustness and reliability of the model.

RESULTS

From 2023 to 2027, the incorporation of PD-1 inhibitor class treatments was anticipated to yield substantial improvements in health outcomes, with an estimated increase of 1,336,332 LYs (+ 24.7%), 1,065,359 QALYs (+ 30.3%), and 1,177,564 PFS years (+ 57.4%) compared to standard of care alone. Simultaneously, the number of grade 3-5 AEs decreased by 334,976 (- 13.0%), and the ORR saw a 19.1% increase (+ 105.6%) relative to standard of care treatment alone.

CONCLUSION

This study provides a analysis of the potential beneficial effects on health outcomes in the Chinese population after introducing PD-1 inhibitor class treatment. The findings suggest the PD-1 inhibitor class will significantly improve patient survival.

Prevalence, predictors and reasons for COVID-19 vaccine hesitancy: Results of a global online survey

Vaccine hesitancy has the potential to cripple efforts to end the COVID-19 pandemic. Policy makers need to be informed about the scale, nature and drivers of this problem, both domestically and globally, so that effective interventions can be designed. To this end, we conducted a statistical analysis of data from the CANDOUR survey (n = 15,536), which was carried out in 13 countries representing approximately half of the global population. Both pooled and country-level ordered regression models were estimated to identify predictors of vaccine hesitancy and reasons for not getting vaccinated. We found high levels of hesitancy, particularly in high-income countries. Factors driving moderate hesitancy differed from those driving extreme hesitancy. A lack of trust in health care providers was consistently the underlying driver of more extreme hesitancy. Predictors of moderate hesitancy varied across countries, though being younger and female was typically associated with greater hesitancy. While political ideology played a role in vaccine hesitancy in some countries, this effect was often moderated by income level, particularly in the US. Overall, the results suggest that different interventions such as mass-media campaigns and monetary incentives may be needed to target the moderately versus extremely hesitant. The lack of trust in health care professionals that drives extreme hesitancy may reflect deep societal mistrust in science and institutions and be challenging to overcome.

Spatial and temporal variability of mercury in Upper and Lower Red Lake Walleye

Mercury is a global pollutant that is released into our environment by natural and anthropogenic processes resulting in extensive studies of mercury cycling in aquatic ecosystems, and the issuance of human-health-based fish-consumption advisories. We examined total mercury concentrations in Walleye Sander vitreus from Upper and Lower Red Lakes, located in north central Minnesota, between 2019 and 2020. Sampled Walleye (n = 265) ranged from 158 to 610 mm in total length from an age range of young-of-the year to 16 years. Mercury concentrations within the Walleye ranged from 0.030 mg/kg to 0.564 mg/kg (x̄ = 0.179 ± 0.105 mg/kg; x̄ = mean ± sd, all fish-mercury concentrations expressed on wet-weight basis). The best supported model for predicting mercury concentrations in Red Lake Walleye included the independent variables: length, age, sex, and lake basin. This model indicated that there was a significant difference in mercury concentrations between Upper and Lower Red Lake (x̄ = 0.215 ± 0.117 and 0.144 ± 0.077 mg/kg, respectively), and also suggests that individuals who rely on fish for subsistence should target Walleye that are ≤ 400 mm from Lower Red Lake. Observed differences in mercury concentrations could be linked to wetland area influences, fish growth rates, and physicochemical parameters between the two basins. Given that our results illustrated a significant difference in fish-mercury concentrations between basins, future pollutant monitoring efforts should treat Upper and Lower Red Lake as separate lakes and not assume that data from one basin can apply to the other.

Socio-economic accounting of inequalities in excess weight: a population-based analysis

BACKGROUND

The prevalence of excess weight has been increasing globally in the last decades, affecting disproportionally adults from low socio-economic backgrounds and putting undue pressure on health systems and societal resources. In England, tackling unfair and unjust health inequalities is at the heart of national public health policy, and a prerequisite for enabling these decision makers to set policy priorities is an understanding of the prevalence and determinants of excess weight inequalities in their local population.

METHODS

We conducted both pooled (England) and regional-level (nine regions: North-East, North-West, Yorkshire and Humber, East Midlands, West Midlands, East of England, London, South East and South West) analyses of individual level data from a nationally representative sample of adults (N = 6,387). We used the Corrected Concentration Index (CCI) to measure absolute inequalities in excess weight across three dimensions of socio-economic deprivation: neighbourhood-level deprivation, occupational status and educational qualification. We used a Shapley decomposition method to evaluate their relative contribution to inequality.

RESULTS

At a national level, all three dimensions of socio-economic deprivation were found to be positively associated with excess weight across the adult population, as measured by the CCI, with educational qualification ranking first [CCI: -0.090, p < 0.01], closely followed by neighbourhood-level deprivation [CCI: -0.050, p < 0.01]. Large variation was found between regions and genders, with inequality being either considerably higher or exclusively patterned among women. The strongest independent factor contributing to excess weight inequalities was having a long-lasting limiting illness, especially among women and towards the right tail of the excess weight spectrum. Heterogeneous patterns of contribution across the excess weight spectrum were found, however age played a dominant role toward the left tail of the distribution.

CONCLUSIONS

While socio-economic inequalities in excess weight exist in the English adult population, our findings underscore the importance of considering multiple dimensions of deprivation and the unique needs of different populations when developing policies to address overweight and obesity. Targeted interventions for adults with overweight and obesity with long-lasting illnesses and women can generate both short-term and long-term economic benefits, by reducing healthcare costs and increasing workforce productivity.

Consumer preferences for sustainably produced ultra-high-temperature milk in China

Contrary to ongoing declines in per capita milk consumption in the United States and Europe, per capita milk consumption in China is experiencing dramatic increases, making China one of the most dynamic global dairy markets. Meeting the rapid growth in milk demand presents environmental challenges under current dairy farm production in China. This article measures Chinese consumer valuation of environmentally sustainable milk and of correlated attributes such as food safety and geographic origin. The authors used a discrete choice experiment to collect survey data from a stratified sample of respondents in 5 cities. Applying a mixed logit demand model to the data, they estimated the probability of choosing sustainably produced UHT pasteurized milk over conventional milk, as well as consumers' willingness to pay for the sustainably produced milk. Empirical results confirm that, overall, consumers value sustainably produced milk as they are willing to pay a premium of $2.01/L, well above the cost of conventional milk. Consumer segments more likely to purchase sustainably produced milk include the young, males, and childless households, as well as those already concerned about the environment and food safety. In addition, this article also finds that consumers exhibit a strong degree of home bias in that they prefer domestic brands with domestically sourced raw milk. Valuable new knowledge is provided for policy makers, producers, and marketers interested in designing marketing strategies, and for other researchers interested in general food sustainability issues.

Assessing the mental effects of COVID-19-related work on depression among community health workers in Vietnam

BACKGROUND

Community health workers (CHWs) involved in the COVID-19 response might be at increased risk of developing depression, though evidence is scarce. We investigated effects of COVID-19-related work on changes in depression levels among CHWs in Vietnam and identified sub-groups among CHWs who are at particular risk of developing severe depression.

METHODS

We conducted a cross-sectional online survey among 979 CHWs who were involved in the COVID-19 response in Vietnam, in particular during the 2021 Tet holiday outbreak between January and March 2021. Respondents were asked to report depression symptoms at two-time points, before the start of the COVID-19 pandemic (average June to December 2019) and during the 2021 Tet holiday outbreak using the PHQ-9 mental health questionnaire. We estimated depression levels at both time points and developed univariate and multivariable logistic regression models to estimate odds ratios and 95% confidence intervals (95% CIs) to explore the association between deterioration to high depression levels and selected risk factors.

RESULTS

Median depression levels among CHWs in Vietnam doubled from 3 (IQR = 2-7) before COVID-19 to 6 (IQR = 3-9) on the PHQ-9 scale during the Tet holiday outbreak. The proportion with normal/minimal levels decreased from 77.1% (95% CI = 74.4-79.7) to 50.9% (95% CI = 47.7-54) (p-value < 0.001), while the proportion of CHWs with moderate, moderately severe, and severe depression levels increased 4.3, 4.5, and five-fold, respectively. Less sleep and poor sleep quality, working in unfavorable work environments, and being involved in contact tracing and the organization of quarantine for suspected cases were associated with an increased risk of deterioration to high depression levels.

CONCLUSIONS

We found a substantial increase in overall depression levels among CHWs in Vietnam due to their COVID-19 related work and a particularly worrisome rise in CHWs suffering from severe depression. CHWs are an indispensable yet often overlooked cadre of work in many low- and middle-income countries and shoulder a heavy psychological burden during the COVID-19 pandemic. Targeted psychological support for CHWs is needed to improve their mental health and to ensure the sustainability of community-based health interventions during COVID-19 and future epidemics.

Avoiding Bias in Measurements of Fundamental Constants from High Resolution Quasar Spectra

Recent advances in spectroscopic instrumentation and calibration methods dramatically improve the quality of quasar spectra. Supercomputer calculations show that, at high spectral resolution, procedures used in some previous analyses of spacetime variations of fundamental constants are likely to generate spurious measurements, biased systematically towards a null result. Developments in analysis methods are also summarised and a prescription given for the analysis of new and forthcoming data.

Are pre- and early pregnancy lifestyle factors associated with the risk of preterm birth? A secondary cohort analysis of the cluster-randomised GeliS trial

Background

Maternal lifestyle is discussed as a modifiable determinant in the prevention of preterm birth. However, previous research on associations between individual lifestyle factors and preterm birth risk is inconclusive. In this secondary analysis, we investigated the associations between several modifiable antenatal lifestyle factors and the odds of preterm birth.

Methods

This secondary cohort analysis used data from the cluster-randomised controlled “healthy living in pregnancy” (GeliS) trial. Data were collected from early pregnancy to birth with maternity records, validated questionnaires and birth protocols. Women with complete datasets for all covariates were eligible for analysis. Multivariate logistic regression models, adjusted for recognised risk factors, were fitted to determine whether dietary quality, assessed with a healthy eating index (HEI), physical activity (PA) levels and antenatal anxiety/distress influenced the odds of preterm birth. Moreover, the combined association between pre-pregnancy body mass index (BMI) and HEI on the odds of preterm birth was explored. The independent associations of individual dietary components and types of PA on prematurity were assessed by adjusted logistic regression models.

Results

Overall, 1738 women were included in the analysis. A low HEI significantly increased the odds of preterm birth (OR 1.54 (CI 1.04 – 2.30), p = 0.033), while no associations with either low PA levels or antenatal anxiety/distress were observed. BMI significantly interacted with HEI on the association with prematurity (p = 0.036). Energy % from protein and the intake of average portions of vegetables and cereals were significantly negatively associated with the odds of preterm birth. There was no significant evidence of an association between different types of PA and prematurity.

Conclusions

This cohort analysis revealed that low dietary quality in early pregnancy may increase the chance of giving birth prematurely, while healthier dietary choices may help to prevent preterm birth. More research on pre- and early pregnancy modifiable lifestyle factors is warranted.

Trial registration

This trial is registered with the Clinical Trial Registry ClinicalTrials.gov (NCT01958307). Registration date 09 October 2013, retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-022-04513-5.

Improved Treatment of the Independent Variables for the Deployment of Model Selection Criteria in the Analysis of Complex Systems

Model selection criteria are widely used to identify the model that best represents the data among a set of potential candidates. Amidst the different model selection criteria, the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) are the most popular and better understood. In the derivation of these indicators, it was assumed that the model’s dependent variables have already been properly identified and that the entries are not affected by significant uncertainties. These are issues that can become quite serious when investigating complex systems, especially when variables are highly correlated and the measurement uncertainties associated with them are not negligible. More sophisticated versions of this criteria, capable of better detecting spurious relations between variables when non-negligible noise is present, are proposed in this paper. Their derivation is obtained starting from a Bayesian statistics framework and adding an a priori Chi-squared probability distribution function of the model, dependent on a specifically defined information theoretic quantity that takes into account the redundancy between the dependent variables. The performances of the proposed versions of these criteria are assessed through a series of systematic simulations, using synthetic data for various classes of functions and noise levels. The results show that the upgraded formulation of the criteria clearly outperforms the traditional ones in most of the cases reported.

Copula based post-processing for improving the NMME precipitation forecasts

Using reliable and timely precipitation forecasts on a monthly or seasonal scale could be useful in many water resources management planning, especially in countries facing drought challenges. Amongst many, the North American Multi-Model Ensemble (NMME) is one of the most well-known models. In this study, a Bayesian method based on Copula functions has been applied to improve NMME precipitation forecasts. This method is based on the existence of a correlation between the raw forecast and observational data. Two main factors affect the results of rainfall improvement based on the selected method. This research has presented innovative methods in these regards namely; 1) the approach of selecting the appropriate statistical distribution for variables and 2) the selection method of improved data according to the conditional probability distribution functions (CPDF). To evaluate the effectiveness of the statistical distribution, firstly the precipitation forecast improvement model has been developed based on the application of parametric (Exponential, Normal, Gamma, LogNormal and General Exreteme Value (GEV)) and non-parametric distributions (Standard Normal Kernel). Then the novel mixed distribution function based on GEV parametric distribution and Standard Normal Kernel (non-parametric distribution) has been suggested. As the second aim, a new method for selecting improved data based on the center of mass of estimated CPDF is presented. The evaluation of the proposed method for estimating the statistical distribution of data and improving the forecast precipitation by the NMME model has been performed in Sistan and Baluchestan province in Iran. In this regard, the data of 1982–2010 for the calibration period and the data of 2012–2016 for the validation of the results have been used. According to the results, the non-parametric distribution best fitted with the data in the time series and selecting the appropriate bandwidth increased the efficiency of this distribution. Besides, due to the weakness of non-parametric distributions in the boundaries, the use of GEV distribution with a high ability to estimate boundary conditions as semi-parametric distribution, led to improved performance of the proposed distribution. Finally, the selection of the improved data based on the center of the mass method has efficiently provided much improvement compared to the maximum likelihood method commonly used.