Effect of Ozone Therapy on Diabetes-related Foot Ulcer Outcomes: A Systematic Review and Meta-analysis.

PURPOSE: This study aimed to assess the effectiveness of ozone therapy in treating Diabetes-related Foot Ulcer (DFU) and its outcomes. METHODS: A systematic search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and ProQuest databases for published studies evaluating the use of ozone as an adjunct treatment for DFU, from inception to December 21, 2022. The primary outcome measure was the change in wound size after the intervention compared to pretreatment. Secondary outcomes included time to complete ulcer healing, number of healed patients, adverse events, amputation rates, and hospital length of stay. Quantitative data synthesis for the meta-analysis was performed using a random-effects model and generic inverse variance method, while overall heterogeneity analysis was conducted using a fixed-effects model. Interstudy heterogeneity was assessed using the I2 index (<50%) and the Cochrane Q statistic test. Sensitivity analysis was performed using the leave-one-out method. RESULTS: The meta-analysis included 11 studies comprising 960 patients with DFU. The results demonstrated a significant positive effect of ozone therapy on reducing foot ulcer size (Standardized Mean Difference (SMD): -25.84, 95% CI: -51.65 to -0.04, p = 0.05), shortening mean healing time (SMD: -38.59, 95% CI: -51.81 to -25.37, p < 0.001), decreasing hospital length of stay (SMD: -8.75, 95% CI: -14.81 to -2.69, p < 0.001), and reducing amputation rates (Relative Risk (RR): 0.46, 95% CI: 0.30-0.71, p < 0.001), compared to standard treatment. CONCLUSION: This meta-analysis indicates that ozone therapy has additional benefits in expediting complete DFU healing, reducing the amputation rates, and decreasing hospital length of stay, though its effects do not differ from standard treatments for complete ulcer resolution. Further research is needed to address the heterogeneity among studies and to better understand the potential beneficial effects of ozone therapy.

A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data.

In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariates have, however, several limitations; they can, for example, not be included when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models, where two or more longitudinal variables are treated as a response and modeled with a correlated random effect. Next, by conditioning on these response(s), we can study the effect of one or more longitudinal variables on another. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the model-based correlations between the responses on their original scale. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response. Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology is applied to a case study where, among others, a longitudinal ordinal response is predicted with a longitudinal continuous variable.

Multilevel superposition for deciphering the conformational variability of protein ensembles.

The dynamics and variability of protein conformations are directly linked to their functions. Many comparative studies of X-ray protein structures have been conducted to elucidate the relevant conformational changes, dynamics and heterogeneity. The rapid increase in the number of experimentally determined structures has made comparison an effective tool for investigating protein structures. For example, it is now possible to compare structural ensembles formed by enzyme species, variants or the type of ligands bound to them. In this study, the author developed a multilevel model for estimating two covariance matrices that represent inter- and intra-ensemble variability in the Cartesian coordinate space. Principal component analysis using the two estimated covariance matrices identified the inter-/intra-enzyme variabilities, which seemed to be important for the enzyme functions, with the illustrative examples of cytochrome P450 family 2 enzymes and class A $\beta$-lactamases. In P450, in which each enzyme has its own active site of a distinct size, an active-site motion shared universally between the enzymes was captured as the first principal mode of the intra-enzyme covariance matrix. In this case, the method was useful for understanding the conformational variability after adjusting for the differences between enzyme sizes. The developed method is advantageous in small ensemble-size problems and hence promising for use in comparative studies on experimentally determined structures where ensemble sizes are smaller than those generated, for example, by molecular dynamics simulations.

Conditioning on the Pre-Test versus Gain Score Modelling: Revisiting the Controversy in a Multilevel Setting.

We consider estimating the effect of a treatment on a given outcome measured on subjects tested both before and after treatment assignment in observational studies. A vast literature compares the competing approaches of modelling the post-test score conditionally on the pre-test score versus modelling the difference, namely, the gain score. Our contribution lies in analyzing the merits and drawbacks of two approaches in a multilevel setting. This is relevant in many fields, such as education, where students are nested within schools. The multilevel structure raises peculiar issues related to contextual effects and the distinction between individual-level and cluster-level treatments. We compare the two approaches through a simulation study. For individual-level treatments, our findings align with existing literature. However, for cluster-level treatments, the scenario is more complex, as the cluster mean of the pre-test score plays a key role. Its reliability crucially depends on the cluster size, leading to potentially unsatisfactory estimators with small clusters.

Meta-analysis prediction intervals are under reported in sport and exercise medicine.

AIM: Prediction intervals are a useful measure of uncertainty for meta-analyses that capture the likely effect size of a new (similar) study based on the included studies. In comparison, confidence intervals reflect the uncertainty around the point estimate but provide an incomplete summary of the underlying heterogeneity in the meta-analysis. This study aimed to estimate (i) the proportion of meta-analysis studies that report a prediction interval in sports medicine; and (ii) the proportion of studies with a discrepancy between the reported confidence interval and a calculated prediction interval. METHODS: We screened, at random, 1500 meta-analysis studies published between 2012 and 2022 in highly ranked sports medicine and medical journals. Articles that used a random effect meta-analysis model were included in the study. We randomly selected one meta-analysis from each article to extract data from, which included the number of estimates, the pooled effect, and the confidence and prediction interval. RESULTS: Of the 1500 articles screened, 866 (514 from sports medicine) used a random effect model. The probability of a prediction interval being reported in sports medicine was 1.7% (95% CI = 0.9%, 3.3%). In medicine the probability was 3.9% (95% CI = 2.4%, 6.6%). A prediction interval was able to be calculated for 220 sports medicine studies. For 60% of these studies, there was a discrepancy in study findings between the reported confidence interval and the calculated prediction interval. Prediction intervals were 3.4 times wider than confidence intervals. CONCLUSION: Very few meta-analyses report prediction intervals and hence are prone to missing the impact of between-study heterogeneity on the overall conclusions. The widespread misinterpretation of random effect meta-analyses could mean that potentially harmful treatments, or those lacking a sufficient evidence base, are being used in practice. Authors, reviewers, and editors should be aware of the importance of prediction intervals.

Profit efficiency among kenyan maize farmers.

The profit efficiency (PE) of maize farming and its determinants are estimated using the true random effect (TRE) approach. A survey of maize farmers was conducted in Uasin Gishu, one of Kenya's top maize-producing regions. Clearly, maize farmers can increase their profits based on the mean PE of 0.62. In terms of profitability, maize farming is elastically affected by the price of maize, but inelastically affected by the price of inputs. In households where the head of household is male, household sizes are larger, and farm sizes are larger, inefficiencies of profit are significantly reduced. Despite this, factors such as the distance between home and the maize farm, soil characteristics, maize diseases, along with natural disasters significantly increase profit inefficiency. According to the findings of the study, maize prices are more effective targets for developing supportive policies than input prices. To significantly increase PE, farmers would benefit from programs designed to improve their production and management skills to preserve soil health and minimize damage caused by disease and natural disasters. Furthermore, increase in PE would be achieved by improving farm size through land-use policies.

Risk factors for herpes zoster infections: a systematic review and meta-analysis unveiling common trends and heterogeneity patterns.

PURPOSE: The burden of herpes zoster (HZ) is substantial and numerous chronic underlying conditions are known as predisposing risk factors for HZ onset. Thus, a comprehensive study is needed to synthesize existing evidence. This study aims to comprehensively identify these risk factors. METHODS: A systematic literature search was done using MEDLINE via PubMed, EMBASE and Web of Science for studies published from January 1, 2003 to January 1, 2023. A random-effects model was used to estimate pooled Odds Ratios (OR). Heterogeneity was assessed using the I2 statistic. For sensitivity analyses basic outlier removal, leave-one-out validation and Graphic Display of Heterogeneity (GOSH) plots with different algorithms were employed to further analyze heterogeneity patterns. Finally, a multiple meta-regression was conducted. RESULTS: Of 6392 considered records, 80 were included in the meta-analysis. 21 different conditions were identified as potential risk factors for HZ: asthma, autoimmune disorders, cancer, cardiovascular disorders, chronic heart failure (CHF), chronic obstructive pulmonary disorder (COPD), depression, diabetes, digestive disorders, endocrine and metabolic disorders, hematological disorders, HIV, inflammatory bowel disease (IBD), mental health conditions, musculoskeletal disorders, neurological disorders, psoriasis, renal disorders, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE) and transplantation. Transplantation was associated with the highest risk of HZ (OR = 4.51 (95% CI [1.9-10.7])). Other risk factors ranged from OR = 1.17-2.87, indicating an increased risk for all underlying conditions. Heterogeneity was substantial in all provided analyses. Sensitivity analyses showed comparable results regarding the pooled effects and heterogeneity. CONCLUSIONS: This study showed an increased risk of HZ infections for all identified factors.

Individual Mobility across Clusters: The Impact of Ignoring Cross-Classified Data Structures in Discrete-Time Survival Analysis.

A multilevel-discrete time survival model may be appropriate for purely hierarchical data, but when data are non-purely hierarchical due to individual mobility across clusters, a cross-classified discrete time survival model may be necessary. The purpose of this research was to investigate the performance of a cross-classified discrete-time survival model and assess the impact of ignoring a cross-classified data structure on the model parameters of a conventional discrete-time survival model and a multilevel discrete-time survival model. A Monte Carlo simulation was used to examine the performance of three discrete-time survival models when individuals are mobile across clusters. Simulation factors included the value of the between-clusters variance, number of clusters, within-cluster sample size, Weibull scale parameter, and mobility rate. The results suggest that substantial relative parameter bias, unacceptable coverage of the 95% confidence intervals, and severely biased standard errors are possible for all model parameters when a discrete-time survival model is used that ignores the cross-classified data structure. The findings presented in this study are useful for methodologists and practitioners in educational research, public health, and other social sciences where discrete-time survival analysis is a common methodological technique for analyzing event-history data.

Postpartum pain and the risk of postpartum depression: A meta-analysis of observational studies.

OBJECTIVE: This meta-analysis of observational studies aimed to derive a more precise estimation of the relationship between postpartum pain and postpartum depression (PPD). METHODS: A systematic literature search was completed in the following databases from inception to September 26, 2022: PubMed, Embase, and Web of Science. Quality evaluation of each study was achieved through Newcastle-Ottawa scale (NOS) assessment. Heterogeneity across studies was evaluated by Cochran's Q test and I2 test. Pooled estimates of odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were analyzed using fixed-effects model or random-effects model, according to heterogeneity. Subgroup analysis, sensitivity analysis, and Egger's test were also performed. RESULTS: From the identified 1884 articles, a total of 8 studies involving 3973 participants were included in the final meta-analysis. Seven of the 8 studies were evaluated as high-quality, with NOS scores ≥7. A significant heterogeneity was observed (I2 = 66.5%, p = 0.004) among eight studies. Therefore, the performed random-effect model suggested a significant association between postpartum pain and PPD risk (OR 1.29, 95% CI 1.10-1.52, p = 0.002). However, the subgroup analyses did not define the source of heterogeneity. Moreover, the sensitivity analysis showed the stability of the pooled results, but the significant publication bias was identified (p = 0.009). The trim and fill method was performed and resulted in an OR of 1.14 (95% CI 0.95-1.37, p = 0.162). CONCLUSIONS: This meta-analysis found a potential association between postpartum pain and PPD. Further researches are needed to provide more robust evidences.

How trace plots help interpret meta-analysis results.

The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of τ , the between-study standard deviation, and the shrunken estimates of the study effects as a function of τ . With a small or moderate number of studies, τ is not estimated with much precision, and parameter estimates and shrunken study effect estimates can vary widely depending on the correct value of τ . The trace plot allows visualization of the sensitivity to τ along with a plot that shows which values of τ are plausible and which are implausible. A comparable frequentist or empirical Bayes version provides similar results. The concepts are illustrated using examples in meta-analysis and meta-regression; implementation in R is facilitated in a Bayesian or frequentist framework using the bayesmeta and metafor packages, respectively.

Association between chronotype and metabolic-associated fatty liver disease in employed adults: A longitudinal study in Southwestern China.

The circadian system is an essential physiological regulator of mammals, and sleep chronotype may be associated with the risk of metabolic disorders. However, evidence regarding the role of sleep chronotype in the development of metabolic-associated fatty liver disease (MAFLD) is scarce, particularly in employed adults. We conducted a longitudinal study of 1,309 employed adults in Southwestern China with a five-year follow-up from 2017 to 2021. MAFLD was assessed by the presence of hepatic steatosis using abdominal ultrasonography, overweight/obese status, diabetes mellitus, metabolic dysregulation, or elevation of high-sensitivity C-reactive protein. Chronotype was assessed by the Morning and Evening Questionnaire-5 (MEQ-5). The logistic random effects model was applied to analyze the 5-year panel data to estimate the association between chronotype and MAFLD, and the potential effect modification of demographics on such association. The MAFLD prevalence of participants was 38.6% at baseline and showed an increasing trend during follow-up (p for trends < 0.05). Compared with morning chronotype, evening chronotype was positively associated with MAFLD (OR = 2.19, 95%CI: [1.09, 4.40]) after controlled for covariates. Age, sex, ethnicity, and educational level did not modify the association between chronotype and MAFLD. These findings suggest that improving circadian rhythms could reduce the risk of MAFLD and chronic disease burden among employed adults.

Separating Measurement Error and Signal in Environmental Data: Use of Replicates to Address Uncertainty.

Measurement uncertainty has long been a concern in the characterizing and interpreting environmental and toxicological measurements. We compared statistical analysis approaches when there are replicates: a Naïve approach that omits replicates, a Hybrid approach that inappropriately treats replicates as independent samples, and a Measurement Error Model (MEM) approach in a random effects analysis of variance (ANOVA) model that appropriately incorporates replicates. A simulation study assessed the effects of sample size and levels of replication, signal variance, and measurement error on estimates from the three statistical approaches. MEM results were superior overall with confidence intervals for the observed mean narrower on average than those from the Naïve approach, giving improved characterization. The MEM approach also featured an unparalleled advantage in estimating signal and measurement error variance separately, directly addressing measurement uncertainty. These MEM estimates were approximately unbiased on average with more replication and larger sample sizes. Case studies illustrated analyzing normally distributed arsenic and log-normally distributed chromium concentrations in tap water and calculating MEM confidence intervals for the true, latent signal mean and latent signal geometric mean (i.e., with measurement error removed). MEM estimates are valuable for study planning; we used simulation to compare various sample sizes and levels of replication.

Often Overlooked Aspects of Sound Symbolism: The Influence of Participants' Characteristics on Size Ratings.

Sound symbolism is a non-arbitrary mapping between phonetic properties and meanings. The existence and nature of sound symbolism have long been the subject of empirical research. It is rarely recognized, however, that participants' intrinsic characteristics (e.g., age, gender, language knowledge), in addition to the commonly studied phonetic features, may also influence size ratings. Our study aims to empirically investigate the impact of participant-specific characteristics on size ratings: It also aims to examine whether these characteristics have a direct impact when considering the impact of phonetic features or they rather modify the effects of phonetic features. The current research reports a novel analysis of a previously published dataset with new research questions and previously unused (participant-specific) data. We show that (a) the participants' characteristics did not affect overall size ratings; however, (b) in some cases, they modify (intensify or weaken but do not reverse) the effect of phonetic features on size ratings. Our results emphasize a more comprehensive treatment of sound symbolism, one that considers not only phonetic but also non-phonetic factors in sound symbolism research.

Heterogeneity in health funding and disparities in health outcome: a comparison between high focus and non-high focus states in India.

BACKGROUND: The Central Government of India introduced the National Health Mission (NHM) in 2005 to improve health outcomes by enhancing publicly financed (government) health expenditure and health infrastructure at the state level. This study aims to examine the effects of the state-level heterogeneity in publicly financed spending on health services on major health outcomes such as life expectancy, infant mortality rate, child mortality rate, the incidence of malaria, and immunization coverage (i.e., BCG, Polio, Measles, and Tetanus). METHODS: This study investigates the relationships between publicly financed health expenditure and health outcomes by controlling income and infrastructure levels across 28 Indian States from 2005 to 2016. Along with all states, the empirical analysis has also been carried out for high-focus and non-high-focus states as per the NHM fund flow criteria. It has applied panel fixed-effects and random effects model wherever required based on the Hausman test. RESULTS: The empirical results show that publicly financed health expenditure reduces infant mortality, child mortality, and malaria cases. At the same time, it improves life expectancy and immunization coverage in India. It also finds that the relationship between publicly financed health expenditure and health outcomes is weak, especially in the high-focus states. CONCLUSIONS: Given the healthcare need for achieving desirable health outcomes, Indian States should enhance publicly financed expenditure on health services. This study augments essential guidance for implementing public health policies in developing countries.

On the Q statistic with constant weights in meta-analysis of binary outcomes.

BACKGROUND: Cochran's Q statistic is routinely used for testing heterogeneity in meta-analysis. Its expected value (under an incorrect null distribution) is part of several popular estimators of the between-study variance, [Formula: see text]. Those applications generally do not account for use of the studies' estimated variances in the inverse-variance weights that define Q (more explicitly, [Formula: see text]). Importantly, those weights make approximating the distribution of [Formula: see text] rather complicated. METHODS: As an alternative, we are investigating a Q statistic, [Formula: see text], whose constant weights use only the studies' arm-level sample sizes. For log-odds-ratio (LOR), log-relative-risk (LRR), and risk difference (RD) as the measures of effect, we study, by simulation, approximations to distributions of [Formula: see text] and [Formula: see text], as the basis for tests of heterogeneity. RESULTS: The results show that: for LOR and LRR, a two-moment gamma approximation to the distribution of [Formula: see text] works well for small sample sizes, and an approximation based on an algorithm of Farebrother is recommended for larger sample sizes. For RD, the Farebrother approximation works very well, even for small sample sizes. For [Formula: see text], the standard chi-square approximation provides levels that are much too low for LOR and LRR and too high for RD. The Kulinskaya et al. (Res Synth Methods 2:254-70, 2011) approximation for RD and the Kulinskaya and Dollinger (BMC Med Res Methodol 15:49, 2015) approximation for LOR work well for [Formula: see text] but have some convergence issues for very small sample sizes combined with small probabilities. CONCLUSIONS: The performance of the standard [Formula: see text] approximation is inadequate for all three binary effect measures. Instead, we recommend a test of heterogeneity based on [Formula: see text] and provide practical guidelines for choosing an appropriate test at the .05 level for all three effect measures.

Random effects modelling versus logistic regression for the inclusion of cluster-level covariates in propensity score estimation: A Monte Carlo simulation and registry cohort analysis.

Purpose: Surgeon and hospital-related features, such as volume, can be associated with treatment choices and outcomes. Accounting for these covariates with propensity score (PS) analysis can be challenging due to the clustered nature of the data. We studied six different PS estimation strategies for clustered data using random effects modelling (REM) compared with logistic regression. Methods: Monte Carlo simulations were used to generate variable cluster-level confounding intensity [odds ratio (OR) = 1.01-2.5] and cluster size (20-1,000 patients per cluster). The following PS estimation strategies were compared: i) logistic regression omitting cluster-level confounders; ii) logistic regression including cluster-level confounders; iii) the same as ii) but including cross-level interactions; iv), v), and vi), similar to i), ii), and iii), respectively, but using REM instead of logistic regression. The same strategies were tested in a trial emulation of partial versus total knee replacement (TKR) surgery, where observational versus trial-based estimates were compared as a proxy for bias. Performance metrics included bias and mean square error (MSE). Results: In most simulated scenarios, logistic regression, including cluster-level confounders, led to the lowest bias and MSE, for example, with 50 clusters × 200 individuals and confounding intensity OR = 1.5, a relative bias of 10%, and MSE of 0.003 for (i) compared to 32% and 0.010 for (iv). The results from the trial emulation also gave similar trends. Conclusion: Logistic regression, including patient and surgeon-/hospital-level confounders, appears to be the preferred strategy for PS estimation.

Prevalence of Type 2 Diabetes Mellitus in the General Population of Saudi Arabia, 2000-2020: A Systematic Review and Meta-Analysis of Observational Studies.

Background: The prevalence of type 2 diabetes mellitus (T2DM) has increased worldwide, including in Saudi Arabia. Objective: To systematically review the available literature and assess the pooled prevalence of T2DM in Saudi Arabia between 2000 and 2020. Methods: Observational studies that reported quantitative estimates of the prevalence of T2DM as their main outcome, included the general population of Saudi Arabia, and were published between 2000-2020 and in English were retrieved using three electronic databases (namely, CINAHL, Medline via PubMed, and Web of Science). Retrieved studies were screened, and relevant data were extracted. The Joanna Briggs Institute Critical Appraisal guideline was used to assess the methodological quality of included studies. A random-effects model was used to estimate the prevalence of T2DM. Results: Twenty-three studies were included in the systematic review, of which 19 were included in the meta-analysis (total pooled population: 258,283). The overall pooled prevalence of T2DM in Saudi Arabia was 16.4% (95% CI: 11.6-17.5). However, there was heterogeneity in the results of the studies [I2 = 99.31%, P < 0.0001] and the summary values varied from 3.18% (95% CI: 1.46-5.95) to 94.34% (95% CI: 89.53-97.38). Although the prevalence of T2DM by age varied across studies, in most studies, it was higher among the older age groups. In addition, the prevalence of diabetes widely varied across the different geographical regions of Saudi Arabia. Conclusions: This is the first meta-analysis that determined the pooled prevalence of T2DM in Saudi Arabia, and it revealed a high prevalence over the past two decades. However, owing to data collection inconsistencies in the identified studies, neither the modifiable (such as obesity, educational status, emotional support, etc.) nor the non-modifiable (such as gender and age) risk factors of T2DM could be determined, thereby indicating the need for a nationally collective effort in determining these factors.

Outlier identification and monitoring of institutional or clinician performance: an overview of statistical methods and application to national audit data.

BACKGROUND: Institutions or clinicians (units) are often compared according to a performance indicator such as in-hospital mortality. Several approaches have been proposed for the detection of outlying units, whose performance deviates from the overall performance. METHODS: We provide an overview of three approaches commonly used to monitor institutional performances for outlier detection. These are the common-mean model, the 'Normal-Poisson' random effects model and the 'Logistic' random effects model. For the latter we also propose a visualisation technique. The common-mean model assumes that the underlying true performance of all units is equal and that any observed variation between units is due to chance. Even after applying case-mix adjustment, this assumption is often violated due to overdispersion and a post-hoc correction may need to be applied. The random effects models relax this assumption and explicitly allow the true performance to differ between units, thus offering a more flexible approach. We discuss the strengths and weaknesses of each approach and illustrate their application using audit data from England and Wales on Adult Cardiac Surgery (ACS) and Percutaneous Coronary Intervention (PCI). RESULTS: In general, the overdispersion-corrected common-mean model and the random effects approaches produced similar p-values for the detection of outliers. For the ACS dataset (41 hospitals) three outliers were identified in total but only one was identified by all methods above. For the PCI dataset (88 hospitals), seven outliers were identified in total but only two were identified by all methods. The common-mean model uncorrected for overdispersion produced several more outliers. The reason for observing similar p-values for all three approaches could be attributed to the fact that the between-hospital variance was relatively small in both datasets, resulting only in a mild violation of the common-mean assumption; in this situation, the overdispersion correction worked well. CONCLUSION: If the common-mean assumption is likely to hold, all three methods are appropriate to use for outlier detection and their results should be similar. Random effect methods may be the preferred approach when the common-mean assumption is likely to be violated.