Credible Interval Research Articles

A municipality-specific analysis to investigate persistent increased incidence rates of childhood leukaemia near the nuclear power plant of Krümmel in Germany.

Increased incidence rates for childhood leukaemia have been reported in municipalities close to the nuclear power plant (NPP) Krümmel (Geesthacht, Germany). Methodological challenges arise when analysing this association at ecological level. They include the use of an appropriate reference population, unstable estimates of standardised incidence ratios (SIRs), and the potential role of prevailing winds. The aim of our study is to address these challenges. The German Childhood Cancer Registry provided data on leukaemia in children under 15years (2004-2019). The German Federal Statistical Office provided the population data. The study region included all municipalities with ≥ 75% surface area within 50kms from the Krümmel NPP. We calculated SIRs using national and regional reference rates. Smoothed incidence relative rates (IRRs) were calculated and mapped to check for potential patterns associated with prevailing winds. Overall 356 cases of childhood leukaemia were observed in the study region (321 municipalities) during 2004-2019. SIRs based on national reference rates show nearly no difference to those calculated using the regional rates as reference. Increased SIR and IRR were observed in Geesthacht (observed-cases = eight; SIR = 2.29; 95% confidence interval: 0.99-4.51. IRR = 1.80; 95% credibility interval: 0.88-2.79). The analysis of the IRR map does not show patterns associated with prevailing winds. Using a regional population as the reference, we found evidence that there may still be an increased risk for childhood leukaemia in Geesthacht. However, IRR estimates are uncertain and credibility intervals are compatible with the absence of elevated risk. The persistent evidence of risk of childhood leukaemia in Geesthacht warrants further epidemiological surveillance.

Estimating the Lifetime Parameters of the Odd-Generalized-Exponential–Inverse-Weibull Distribution Using Progressive First-Failure Censoring: A Methodology with an Application

This paper investigates statistical methods for estimating unknown lifetime parameters using a progressive first-failure censoring dataset. The failure mode’s lifetime distribution is modeled by the odd-generalized-exponential–inverse-Weibull distribution. Maximum-likelihood estimators for the model parameters, including the survival, hazard, and inverse hazard rate functions, are obtained, though they lack closed-form expressions. The Newton–Raphson method is used to compute these estimations. Confidence intervals for the parameters are approximated via the normal distribution of the maximum-likelihood estimation. The Fisher information matrix is derived using the missing information principle, and the delta method is applied to approximate the confidence intervals for the survival, hazard rate, and inverse hazard rate functions. Bayes estimators were calculated with the squared error, linear exponential, and general entropy loss functions, utilizing independent gamma distributions for informative priors. Markov-chain Monte Carlo sampling provides the highest-posterior-density credible intervals and Bayesian point estimates for the parameters and reliability characteristics. This study evaluates these methods through Monte Carlo simulations, comparing Bayes and maximum-likelihood estimates based on mean squared errors for point estimates, average interval widths, and coverage probabilities for interval estimators. A real dataset is also analyzed to illustrate the proposed methods.

Associations of per- and polyfluoroalkyl substances and heavy metals with blood lipid profiles in a representative sample of Korean adolescents

BackgroundPrevious studies on the associations of per- and polyfluoroalkyl substances (PFASs) and heavy metals with lipid profiles among adolescents have been scarce. We sought to investigate the associations of PFASs and heavy metals with blood lipid levels in a representative sample of Korean adolescents.MethodsData from the Korean National Environmental Health Survey (2018–2020) were used. Concentrations of PFASs [perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), perfluorohexane sulfonic acid, perfluorononanoic acid (PFNA), and perfluorodecanoic acid (PFDeA)], lead, and mercury were measured in serum, whole blood, and urine samples, respectively. Linear regression, Bayesian kernel machine regression (BKMR), and k-means clustering analyses were employed to evaluate the associations between pollutants and lipid levels.ResultsIn the linear regression analyses, PFOA levels were associated with higher low-density lipoprotein cholesterol (LDL-C) levels; PFOS with higher total cholesterol (TC) levels; PFNA with higher TC, LDL-C, and non-high-density lipoprotein cholesterol (non-HDL-C) levels; PFDeA with higher TC, LDL-C, non-HDL-C, and high-density lipoprotein cholesterol levels; and mercury with higher TC and non-HDL-C levels. The BKMR analysis revealed that the PFAS and heavy metal mixture was associated with higher LDL-C levels (1.8% increase in LDL-C at the 75th percentile of all PFAS and heavy metal concentrations compared to their median values, 95% credible interval: 0.5, 3.1), primarily driven by the effect of PFDeA. Compared to individuals in the low pollutant exposure cluster (geometric mean levels of PFOA, PFOS, PFHxS, PFNA, PFDeA, lead, and mercury were 2.7 μg/L, 6.2 μg/L, 1.6 μg/L, 0.7 μg/L, 0.4 μg/L, 0.8 μg/dL, and 0.3 μg/L, respectively), those in the high pollutant exposure cluster (5.1 μg/L, 10.7 μg/L, 3.7 μg/L, 1.3 μg/L, 0.6 μg/L, 0.9 μg/dL, and 0.4 μg/L, respectively) demonstrated higher TC levels (2.5% increase in TC, 95% confidence interval: 0.1, 5.0) in the k-means clustering analysis.ConclusionDue to the limitations of this study, such as its cross-sectional design, these results should be interpreted cautiously and confirmed in future studies before drawing implications for public health strategies aimed at promoting health during adolescence and later in life.

An International Factorial Vignette-Based Survey of Intubation Decisions in Acute Hypoxemic Respiratory Failure.

Intubation is a common procedure in acute hypoxemic respiratory failure (AHRF), with minimal evidence to guide decision-making. We conducted a survey of when to intubate patients with AHRF to measure the influence of clinical variables on intubation decision-making and quantify variability. Factorial vignette-based survey asking "Would you recommend intubation?" Respondents selected an ordinal recommendation from a 5-point scale ranging from "Definite no" to "Definite yes" for up to ten randomly allocated vignettes. We used Bayesian proportional odds modeling, with clustering by individual, country, and region, to calculate mean odds ratios (ORs) with 95% credible intervals (CrIs). Anonymous web-based survey. Clinicians involved in the decision to intubate. None. Between September 2023 and January 2024, 2,294 respondents entered 17,235 vignette responses in 74 countries (most common: Canada [29%], United States [26%], France [9%], Japan [8%], and Thailand [5%]). Respondents were attending physicians (63%), nurses (13%), trainee physicians (9%), respiratory therapists (9%), and other (6%). Lower oxygen saturation, higher Fio2, noninvasive ventilation compared with high-flow, tachypnea, neck muscle use, abdominal paradox, drowsiness, and inability to obey were associated with increased odds of intubation; diagnosis, vasopressors, and duration of symptoms were not. Nurses were less likely than physicians to recommend intubation. Within a country, the odds of recommending intubation changed between clinicians by an average factor of 2.60; within a region, the same odds changed between countries by 1.56. Respondents from Canada (OR, 0.53; CrI, 0.40-0.70) and the United States (OR, 0.63; CrI, 0.48-0.84) were less likely to recommend intubation than respondents from most other countries. In this international, multiprofessional survey of 2294 clinicians, intubation for patients with AHRF was mostly decided based on oxygenation, breathing pattern, and consciousness, but there was important variation across individuals and countries.

Implementation of a National Prenatal Exome Sequencing Service in England: Cost-Effectiveness Analysis.

Prenatal exome sequencing (pES) for diagnosing fetal structural anomalies commenced in the English National Health Service (NHS) in 2020. We evaluated cost-effectiveness to the healthcare system, and costs to families, of pES in addition to standard testing, compared to standard testing alone. A cost-effectiveness analysis combining costs, outcomes, parent and professional interview and professional survey data. The English NHS Genomic Medicine Service. 413 families with fetal anomalies with a suspected genetic cause referred for pES from 01 October 2021 to 30 June 2022. We costed the incremental resource required to deliver the pES clinical pathway. We calculated the diagnostic yield (proportion of cases with pathogenic variants). We divided the total incremental cost by the number of cases with a diagnosis to calculate cost-effectiveness. We estimated the annual NHS budget requirement based on case numbers. We determined parental costs from interviews. Incremental costs of pES to the NHS and families, incremental cost per additional diagnosis and NHS budget impact. Of 413 referred cases, 241 were tested, at a cost of £2331 (95% credibility interval £1894-£2856) per referred case or £3592 (£2959-£4250) per case that proceeded with testing. The incremental cost per diagnosis (yield 35.3%) was £11 326 (£8582-£15 361). Based on referrals data 01 October 2022 to 30 September 2023, pES costs the NHS £1.8 m annually. Family costs could not be separated from other pregnancy-related appointments but were not considered burdensome; most appointments were concurrent or remote. pES costs the English NHS £11 326 for each additional diagnosis. Incremental costs to families are negligible.

State-level disparities in cervical cancer prevention and outcomes in the U.S.: a modeling study.

Despite HPV vaccines' availability for over a decade, coverage across the US varies. While some states have tried to increase HPV vaccination coverage, most model-based analyses focus on national impacts. We evaluated hypothetical changes in HPV vaccination coverage at the national and state levels for California, New York, and Texas using a mathematical model. We developed a new mathematical model of HPV transmission and cervical cancer, creating US and state-level models, incorporating country- and state-specific vaccination coverage and cervical cancer incidence and mortality. We quantified the national and state-level impact of increasing HPV vaccination coverage to 80% by 2025 or 2030 on cervical cancer outcomes and the time to elimination defined as < 4 per 100k women. Increasing vaccination coverage to 80% in Texas over ten years could reduce cervical cancer incidence by 50.9% (95% credible interval [CrI]: 46.6-56.1%) by 2100, from 1.58 (CrI : 1.19-2.09) to 0.78 (CrI : 0.57-1.02) per 100,000 women. Similarly, New York could see a 27.3% (CrI : 23.9-31.5%) reduction, from 1.43 (CrI : 0.93-2.07) to 1.04 (Crl : 0.66-1.53) per 100,000 women, and California a 24.4% (CrI : 20.0-30.0%) reduction, from 1.01 (Crl : 0.66-1.44) to 0.76 (Crl : 0.50-1.09) per 100,000 women. Achieving 80% coverage in five years will provide slightly larger and sooner reductions. If the vaccination coverage levels in 2019 continue, cervical cancer elimination could occur nationally by 2051 (Crl : 2034-2064), but state timelines may vary by decades. Targeting an HPV vaccination coverage of 80% by 2030 will disproportionately benefit states with low coverage and higher cervical cancer incidence. Geographically focused analyses can better inform priorities.

Multispectral Sirens: Gravitational-wave Cosmology with (Multi-) Subpopulations of Binary Black Holes

The cosmic expansion rate can be directly measured with gravitational-wave (GW) data of the compact binary mergers by jointly constraining the mass function of the population and the cosmological model via the so-called spectral sirens. Such a method relies on the features in the mass functions, which may originate from some individual subpopulations and hence become blurred/indistinct due to the superposition of different subpopulations. In this work, we propose a novel approach to constrain the cosmic expansion rate with subpopulations of GW events, named multispectral sirens. The advantage of the multispectral sirens compared to the traditional spectral sirens is demonstrated by the simulation with the mock data. The application of this approach to the GWTC-3 data yields H0=73.3−25.6+29.9Mpc−1kms−1 (median and symmetric 68.3% credible interval), which is about 19% tighter than the result inferred with the traditional spectral sirens utilizing a powerlaw+peak mass function. The incorporation of the bright standard siren GW170817 with a uniform prior in [10, 200] (log-uniform prior in [20,140]) Mpc−1 km s−1 gives H0=71.1−7.5+15.0(70.3−7.1+12.9)Mpc−1kms−1 (68.3% confidence level), corresponding to an improvement of ∼26% (23%) with respect to the measurement from sole GW170817.

Variability and correlations among vital rates and their influence on population growth in mule and black‐tailed deer

AbstractTo reverse range‐wide population declines, managers of black‐tailed and mule deer (Odocoileus hemionus) require information on the vital rates and life stages most influential to population growth to target effective management actions. We extracted black‐tailed and mule deer vital rates from a range‐wide literature review and used hierarchical models to summarize vital rates, their variability, and how they correlate with one another. We then used matrix models and life‐stage simulation analysis to determine the individual vital rates that contributed most to annual population growth rate (i.e., lambda). Annual adult female survival explained the greatest amount of variation (62%) in lambda. Annual juvenile survival explained 44% of the variation in lambda, whereas summer or winter juvenile survival by themselves were far less informative. Winter fawn:doe ratios, a metric often collected by management agencies, explained only 10% of the variation in lambda. Given an adult female survival of 0.84, our simulations estimated a lambda of 1.0 (95% credible interval = 0.88–1.14), indicating equal probability that a population would increase or decrease. Simulations further indicated that given adult survival rates &lt;70%, the population would always decline, but as survival increased lambda increased linearly. In contrast, estimates of lambda plateaued when annual juvenile survival reached approximately 0.5, indicating higher survival rates yielded diminishing returns to population stability. Using simulated values within the observed range of vital rate values across the species' geographical distribution, the mean lambda was 0.975 and in 61% of the simulations, lambda was &lt;1. After 20 years, we estimated that this distribution of lambda values would cause populations to decrease in 92% of instances with a mean decrease of 44%. Our results align with observed declines in mule deer populations throughout their range over recent decades and indicate that these trends will continue until management can improve survival of adult females.

A bootstrap procedure to estimate the causal effect of a public policy, considering overlap and imperfect compliance

This paper introduces a nonparametric bootstrap method for estimating the causal effects of public policy under the circumstances of imperfect compliance and overlap. It focuses on business investment subsidies in Sardinia by comparing firms eligible for the 1999 subsidies to those not, amid issues of imperfect compliance and overlapping programs. Bootstrap confidence intervals (CI) are proposed for the average effect of treatment on the sub-population of compliers. The obtained CIs are consistent across nominal levels and robust against data nonnormality; they show coverages of credible intervals close to nominal, suggesting effectiveness for assessing causal effects. Compared to other methods, the results of the new combination of a specific estimator for incompliance and the bootstrap align with those of more modern approaches such as Bayesian Additive Regression Trees and Causal forest.

Blood Pressure Targets for Adults with Vasodilatory Shock - An Individual Patient Data Meta-Analysis.

We sought to estimate whether a lower mean arterial blood pressure target, compared with a higher mean arterial blood pressure target, reduced 90-day all-cause mortality among critically ill adult patients with vasodilatory shock. We conducted an individual patient data meta-analysis of randomized controlled trials that evaluated the effect of distinct thresholds of mean arterial blood pressure to guide vasopressor support among critically ill adults identified in a systematic literature search. The main exposure was a lower mean arterial pressure target compared with a higher mean arterial pressure target (including usual care). The primary outcome was 90-day all-cause mortality. We used a Bayesian random effects log-binomial model to estimate risk ratios with 95% credible intervals (CrIs). Between 2010 and 2019, 3352 patients were randomly assigned in three trials (SEPSISPAM, OVATION pilot trial, and 65-Trial) across 103 hospitals from the United Kingdom, France, and Canada. When compared with a higher mean arterial blood pressure target or usual care, the risk ratio for 90-day all-cause mortality associated with a lower blood pressure target was 0.93 (95% CrI, 0.76 to 1.07; low certainty, posterior probability of benefit 87%). Results were consistent across multiple secondary and sensitivity analyses, including adjustment for prognostically important baseline covariates and alternative modeling techniques. Multiple approaches to evaluate the heterogeneity of treatment effect did not identify any subgroups that may potentially benefit from higher mean arterial blood pressure targets. Targeting a lower mean arterial blood pressure for vasopressor therapy in critically ill patients with vasodilatory shock possibly reduced 90-day all-cause mortality. However, the certainty of evidence is low, and this analysis does not exclude the possibility that lower targets may cause harm overall.

Adherence to Healthy Prepregnancy Lifestyle and Risk of Adverse Pregnancy Outcomes: A Prospective Cohort Study

ABSTRACTObjectiveTo quantify the association between a combination of modifiable prepregnancy lifestyle factors and the risk of adverse pregnancy outcomes (APOs).DesignProspective cohort study.SettingThe Japan Environment and Children's Study.PopulationA total of 79 703 pregnant Japanese women without chronic disease.MethodsMaternal lifestyle before pregnancy was assessed using a self‐administered questionnaire. A healthy lifestyle score (HLS, 0–5 points) was calculated based on adherence to five prepregnancy healthy lifestyle factors: healthy weight, high‐quality diet, regular physical activity, not smoking, and not drinking alcohol. Relative risks (RRs) and 95% credible intervals (CrIs) were estimated using a Bayesian log‐binomial regression model.Main Outcome MeasuresComposite APOs, defined as the development of any APO, including gestational diabetes, hypertensive disorders of pregnancy, preterm birth, low birth weight, and small‐for‐gestational‐age, transcribed from medical records.ResultsA total of 13 894 women (17.4%) experienced one or more APOs. HLS was inversely associated with the risk of APOs in a dose–response manner. Women with an HLS of 5 points had a 33% (RR 0.67; 95% CrI, 0.61–0.74) lower risk of APOs than those with the lowest HLS (0–1 points). The population attributable fraction of five healthy lifestyle factors was 10.3%. A 1‐point increase of HLS could have reduced APO cases by 6.6%.ConclusionsA higher HLS was associated with a lower risk of APOs, suggesting that adopting a healthy lifestyle before pregnancy may reduce the risk of APOs, which can increase the risk of future chronic diseases in both mother and child.

Improving Survey Inference Using Administrative Records Without Releasing Individual-Level Continuous Data.

Probability surveys are challenged by increasing nonresponse rates, resulting in biased statistical inference. Auxiliary information about populations can be used to reduce bias in estimation. Often continuous auxiliary variables in administrative records are first discretized before releasing to the public to avoid confidentiality breaches. This may weaken the utility of the administrative records in improving survey estimates, particularly when there is a strong relationship between continuous auxiliary information and the survey outcome. In this paper, we propose a two-step strategy, where the confidential continuous auxiliary data in the population are first utilized to estimate the response propensity score of the survey sample by statistical agencies, which is then included in a modified population data for data users. In the second step, data users who do not have access to confidential continuous auxiliary data conduct predictive survey inference by including discretized continuous variables and the propensity score as predictors using splines in a Bayesian model. We show by simulation that the proposed method performs well, yielding more efficient estimates of population means with 95% credible intervals providing better coverage than alternative approaches. We illustrate the proposed method using the Ohio Army National Guard Mental Health Initiative (OHARNG-MHI). The methods developed in this work are readily available in the R package AuxSurvey.

Projected Cost Savings With Optimal Medication Adherence in Patients With Cardiovascular Disease Requiring Lipid-Lowering Therapy: A Multinational Economic Evaluation Study.

Poor adherence to chronic cardiovascular treatments can impede targeted clinical outcomes. This study estimates the potential benefits of improving adherence among patients with cardiovascular disease requiring secondary prevention in Mexico, Thailand, and China. We performed Markov model simulation for patients with cardiovascular disease in 3 countries from health care and societal perspectives over a lifetime horizon. Two scenarios were compared: (1) optimal adherence based on a meta-analysis of 51 randomized controlled trials and (2) status quo. The association between adherence and cardiovascular disease outcomes derives from a dose-response meta-analysis of 4 051 338 patients. Outcomes include the accumulated number of cardiovascular events and associated costs in 2022 US dollars, life years, and quality-adjusted life years. Optimal adherence could prevent 42 (95% credible interval [CrI], 29-56) cardiovascular events in Mexico, 34 (95% CrI, 24-50) in Thailand, and 63 (95% CrI, 43-89) in China per 1000 patients over a lifetime. Incremental effectiveness per patient was 0.60 (95% CrI, 0.47-0.74) life-years in Mexico, 0.68 (95% CrI, 0.37-0.94) quality-adjusted life years in Thailand, and 0.93 (95% CrI, 0.44-1.27) quality-adjusted life years in China. Cost savings from societal perspective amounted to $412 (95% CrI, $211-$723), $316 (95% CrI, $187-$541), and $700 (95% CrI, $355-$1144) per patient for Mexico, Thailand, and China, respectively. Findings remained cost saving in deterministic and probabilistic sensitivity analyses. Achieving optimal adherence in patients with cardiovascular disease requiring lipid-lowering therapy saves costs and improves health outcomes in Mexico, Thailand, and China. These findings support national health care systems implementing strategies to improve adherence in these countries.

A Bayesian analysis integrating expert beliefs to better understand how new evidence ought to update what we believe: a use case of chiropractic care and acute lumbar disc herniation with early surgery

BackgroundA Bayesian approach may be useful in the study of possible treatment-related rare serious adverse events, particularly when there are strongly held opinions in the absence of good quality previous data. We demonstrate the application of a Bayesian analysis by integrating expert opinions with population-based epidemiologic data to investigate the association between chiropractic care and acute lumbar disc herniation (LDH) with early surgery.MethodsExperts’ opinions were used to derive probability distributions of the incidence rate ratio (IRR) for acute LDH requiring early surgery associated with chiropractic care. A ‘community of priors’ (enthusiastic, neutral, and skeptical) was built by dividing the experts into three groups according to their perceived mean prior IRR. The likelihood was formed from the results of a population-based epidemiologic study comparing the relative incidence of acute LDH with early surgery after chiropractic care versus primary medical care, with sensitive and specific outcome case definitions and surgery occurring within 8- and 12-week time windows after acute LDH. The robustness of results to the community of priors and specific versus sensitive case definitions was assessed.ResultsThe most enthusiastic 25% of experts had a prior IRR of 0.42 (95% credible interval [CrI], 0.03 to 1.27), while the most skeptical 25% of experts had a prior IRR of 1.66 (95% CrI, 0.55 to 4.25). The Bayesian posterior estimates across priors and outcome definitions ranged from an IRR of 0.39 (95% CrI, 0.21 to 0.68) to an IRR of 1.40 (95% CrI, 0.52 to 2.55). With a sensitive definition of the outcome, the analysis produced results that confirmed prior enthusiasts’ beliefs and that were precise enough to shift prior beliefs of skeptics. With a specific definition of the outcome, the results were not strong enough to overcome prior skepticism.ConclusionA Bayesian analysis integrating expert beliefs highlighted the value of eliciting informative priors to better understand how new evidence ought to update prior existing beliefs. Clinical epidemiologists are encouraged to integrate informative and expert opinions representing the end-user community of priors in Bayesian analyses, particularly when there are strongly held opinions in the absence of definitive scientific evidence.

Preoperative quantitative imaging use in predicting intraoperative decision for hip labral repair versus reconstruction

Abstract Intraoperative assessment of labral quality determines arthroscopic repair versus reconstruction for hip labral tear treatment. T2 mapping technology discriminates between healthy and damaged cartilage. This study investigated if T2 mapping magnetic resonance imaging (MRI) can preoperatively predict labral repair versus reconstruction. This retrospective comparative study included patients with preoperative T2 mapping MRI who underwent hip labral repair or reconstruction at a single institution between March 2021 and February 2023. Three reviewers using Syngo.via recorded average T2 mapping values for the labrum, acetabular cartilage, and femoral cartilage on patients’ sagittal cut. Intraclass correlation values estimated rater agreement of T2 values. T2 means were compared using t-tests. Three Bayesian regression models were created, separately analyzing the labrum, acetabular cartilage, and femoral cartilage mapping values. The 95% credible intervals determined the significance of regression coefficients. A total of 63 operations were included: 14 reconstructions and 49 repairs. Participants were 14- to 50-years-old, with 35 females and 28 males. There was excellent agreement among raters for T2 measurements. There was no significant difference in average T2 values between the repair and reconstruction groups. All three models showed that the odds of labral reconstruction were negatively associated with T2 mapping values, positively associated with age, and increased in males. Preoperative T2 mapping values from the labrum, acetabular, and femoral cartilage are negatively associated with the odds of needing a labral reconstruction. Increased age and being male are associated with increased odds of needing a labral reconstruction. This study will allow further evaluation into other variables that predict labral repair versus reconstruction.

Spinal Cord Stimulation vs Medical Management for Chronic Back and Leg Pain

Chronic back and lower extremity pain is one of the leading causes of disability worldwide. Spinal cord stimulation (SCS) aims to improve symptoms and quality of life. To evaluate the efficacy of SCS therapies compared with conventional medical management (CMM). MEDLINE, Embase, and Cochrane Library were systematically searched from inception to September 2, 2022. Selected studies were randomized clinical trials comparing SCS therapies with sham (placebo) and/or CMM or standard treatments for adults with chronic back or leg pain who had not previously used SCS. Evidence synthesis estimated odds ratios (ORs) and mean differences (MDs) and their associated credible intervals (CrI) through bayesian network meta-analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline for network meta-analyses was followed. The primary outcomes were pain-related end points, including pain intensity (measured by visual analog scale) and proportion of patients achieving at least 50% pain relief (responder rate) in the back or leg. Quality of life (measured by EQ-5D index score) and functional disability (measured by the Oswestry Disability Index score) were also considered. A total of 13 studies of 1561 patients were included in the network meta-analysis comparing conventional and novel SCS therapies with CMM across the 6 outcomes of interest at the 6-month follow-up. Both conventional and novel SCS therapies were associated with superior efficacy compared with CMM in responder rates in back (conventional SCS: OR, 3.00; 95% CrI, 1.49 to 6.72; novel SCS: OR, 8.76; 95% CrI, 3.84 to 22.31), pain intensity in back (conventional SCS: MD, -1.17; 95% CrI, -1.64 to -0.70; novel SCS: MD, -2.34; 95% CrI, -2.96 to -1.73), pain intensity in leg (conventional SCS: MD, -2.89; 95% CrI, -4.03 to -1.81; novel SCS: MD, -4.01; 95% CrI, -5.31 to -2.75), and EQ-5D index score (conventional SCS: MD, 0.15; 95% CrI, 0.09 to 0.21; novel SCS: MD, 0.17; 95% CrI, 0.13 to 0.21). For functional disability, conventional SCS was superior to CMM (MD, -7.10; 95% CrI, -10.91 to -3.36). No statistically significant differences were observed for other comparisons. This systematic review and network meta-analysis found that SCS therapies for treatment of chronic pain in back and/or lower extremities were associated with greater improvements in pain compared with CMM. These findings highlight the potential of SCS therapies as an effective and valuable option in chronic pain management.

Timeliness of lung cancer care and area-level determinants in Victoria: A Bayesian spatio-temporal analysis.

It has been reported that the timeliness of lung cancer care varies significantly across different regions. According to the Victorian Lung Cancer Registry (VLCR) report, the timeliness of lung cancer care in Victoria has changed over time. Therefore, we aimed to quantify the extent of these spatial inequalities over time and to identify area-level determinants contributing to these changes. The study analysed lung cancer cases reported to the VLCR between 2011 and 2022. Bayesian spatio-temporal Conditional Autoregressive (CAR) models were fitted, incorporating spatial random effects, temporal random effects, as well as spatio-temporal interactions. The best-performing model was selected using the Deviance Information Criterion (DIC). For the final best-fit model, the adjusted Relative Risks (aRR) and their 95% Credible Interval (CrI) were reported. Over half (51.24%) of lung cancer patients experienced treatment delays, while approximately one-third (30.98%) encountered diagnostic delays. Moderate spatio-temporal variations were observed in both delayed diagnosis and treatment. In the final best-fit model for treatment delay, an increase in the percentage of smokers was significantly associated with a higher risk of treatment delay (RR = 2.13, 95% CrI: 1.13, 4.20). Identifying high-risk areas provides useful information for policymakers, helping in the reduction of delays in lung cancer diagnosis and treatment. This study has revealed spatio-temporal inequalities in diagnostic and treatment delays, providing valuable insights for identifying areas that should be prioritized to ensure timely care for lung cancer.

Comparative effects of different beverages on weight loss in adults: a systematic review and network meta-analysis of randomized trials.

There is a lack of clarity on the comparative effects of different beverages on weight loss in adults. This study aimed at quantifying and ranking the effects of different beverages on weight loss. We searched PubMed, Scopus, and Web of Science up to January 2023. We included randomized trials evaluating the comparative effects of two or more beverages, or compared a beverage against a control group (water, no intervention), for weight loss in adults. We conducted a random-effects network meta-analysis (NMA) with a Bayesian framework to estimate mean difference [MD] and 95% credible interval [CrI]. In total, 78 randomized trials with 4168 participants were eligible. Low/no-calorie sweetened beverages (LNCSB) was effective for weight loss compared with water (MD: -0.79 kg, 95% CrI: -1.35, -0.18), milk (MD: -0.80 kg, 95% CrI: -1.59, -0.01), fruit juice (MD: -0.83 kg, 95% CrI: -1.47, -0.13), sugar-sweetened beverages (SSB) (MD: -1.08 kg, 95% CrI: -1.65, -0.50), and no intervention (MD: -1.19 kg, 95% CrI: -1.93, -0.41). However, in sensitivity analyses, no significant effect was seen in trials with a low risk of bias and those that implemented calorie restriction. LNCSB drinking was effective for waist circumference reduction compared with water (MD: -1.85 cm, 95% CrI: -3.47, -0.22). The certainty of evidence from most comparisons was rated low. This study suggested evidence of low certainty that intake of LNCSBs can result in a small weight loss when used as a substitute for other beverages. Considering the low certainty of evidence, more research is needed to compare the effects of different beverages on body weight. Not applicable, but the protocol of this systematic review was registered at PROSPERO (registration number: CRD42023407937).

Comparison of different surgical strategies for cervical dystonia: Evidence from Bayesian network analysis.

Several surgical techniques have been used to treat cervical dystonia (CD), however, to date, the optimal surgical technique for CD remains controversial. We therefore conducted the first network meta-analysis to compare different surgical strategies for CD to inform clinical practice. Electronic databases were searched for surgical strategies for treating CD. The primary outcome was improvement in total Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) score. Subgroup analysis was performed to compare short-term (< 1 year) and long-term (≥ 1 year) outcomes. Safety outcomes included surgery-related adverse events (AEs). A total of 55 trials with 2032 patients employing five surgical strategies were identified, including globus pallidus internus (GPi)/subthalamic nucleus (STN)-deep brain stimulation (DBS), selective peripheral denervation (SPD), microvascular decompression (MVD) and pallidotomy. All strategies led to significant improvement in total TWSTRS score (mean improvement range 18.65-28.22). GPi-DBS showed significantly greater enhancement than SPD for the whole dataset (mean difference [MD] 7.03, 95% credible interval [Crl] 1.53-12.56), while both GPi-DBS (MD 8.05, 95% Crl 2.35-13.80) and STN-DBS (MD 10.71, 95% Crl 2.22-19.20) exhibited more long-term improvement than SPD. Regarding safety outcomes, GPi/STN-DBS and MVD were associated with fewer surgery-related AEs than SPD (ln odds ratio range -1.68 to -1.41). We conclude that DBS should be the preferred surgical option for CD, and the STN is a promising alternative target choice due to its comparable efficacy with the GPi. However, more direct evidence is still required.

Comparative Safety of Antipsychotic Medications and Mood Stabilizers During Pregnancy: A Systematic Review and Network Meta-analysis of Congenital Malformations and Prenatal Outcomes.

A network meta-analysis was performed to evaluate the risk of congenital malformations and other prenatal outcomes in fetuses after exposure to antipsychotic medications and mood stabilizers during pregnancy. We searched the PubMed, EMBASE, and Cochrane CENTRAL databases up to 15 December 2023, to identify experimental and observational studies comparing antipsychotic and mood stabilizer treatments with control treatments (no exposure). The primary outcome of the study was the incidence of congenital malformations and the secondary outcomes were preterm birth and spontaneous abortion. Additionally, two authors independently assessed the risk of bias in each domain of the included studies using the ROBINS-I tool and evaluated the quality of evidence using the CINeMA rating tool. The literature search identified 18,334 potential records, and 22 studies involving 3,042,997 pregnant women were ultimately included. Compared with the unexposed group, quetiapine [odds ratio (OR), 1.19; 95% credible interval (CrI), 1.01-1.39], aripiprazole (OR, 1.30; 95% CrI 1.10-1.65), olanzapine (OR, 1.33; 95% CrI 1.11-1.64), risperidone (OR, 1.43; 95% CrI 1.18-1.77), and lithium (OR, 1.61; 95% CrI 1.07-2.30) were associated with a slightly increased risk of congenital malformations. In contrast, lamotrigine (OR, 1.21; 95% CrI 0.86-1.64), ziprasidone (OR, 1.14; 95% CrI 0.73-1.72), and haloperidol (OR, 1.26; 95% CrI 0.90-1.75) did not show significant differences compared with the unexposed group, with narrower credible intervals. The evidence from this analysis suggests that, overall, quetiapine has the lowest teratogenic risk when used during pregnancy, making it thesaferoption for pregnant women. Lamotrigine and haloperidol follow closely behind. At the same time, the use of lurasidone and ziprasidone should be approached with caution, and further clinical studies are necessary to better assess their safety. PROSPERO CRD4201811373.