Influence of Social Vulnerability Index on Medicare Beneficiaries' Expenditures upon Discharge.

Medicare beneficiaries' healthcare spending varies across geographical regions, influenced by availability of medical resources and institutional efficiency. We aimed to evaluate whether social vulnerability influences healthcare costs among Medicare beneficiaries. Multivariable regression analyses were conducted to determine whether the social vulnerability index (SVI), released by the CDC, was associated with average submitted covered charges, total payment amounts, or total covered days upon hospital discharge among Medicare beneficiaries. We used information from discharged Medicare beneficiaries from hospitals participating in the Inpatient Prospective Payment System. Covariate adjustment included demographic information consisting of age groups, race/ethnicity, and Hierarchical Condition Category risk score. The regressions were performed with weights proportioned to the number of discharges. Average submitted covered charges significantly correlated with SVI (ß=0.50, p<0.001) in the unadjusted model and remained significant in the covariates-adjusted model (ß=0.25, p=0.039). The SVI was not significantly associated with the total payment amounts (ß=-0.07, p=0.238) or the total covered days (ß=0.00, p=0.953) in the adjusted model. Regional variations in Medicare beneficiaries' healthcare spending exist and are influenced by levels of social vulnerability. Further research is warranted to fully comprehend the impact of social determinants on healthcare costs.

Assessing the Seasonality of Lab Tests Among Patients with Alzheimer's Disease and Related Dementias in OneFlorida Data Trust.

About 1 in 9 older adults over 65 has Alzheimer's disease (AD), many of whom also have multiple other chronic conditions such as hypertension and diabetes, necessitating careful monitoring through laboratory tests. Understanding the patterns of laboratory tests in this population aids our understanding and management of these chronic conditions along with AD. In this study, we used an unimodal cosinor model to assess the seasonality of lab tests using electronic health record (EHR) data from 34,303 AD patients from the OneFlorida+ Clinical Research Consortium. We observed significant seasonal fluctuations-higher in winter in lab tests such as glucose, neutrophils per 100 white blood cells (WBC), and WBC. Notably, certain leukocyte types like eosinophils, lymphocytes, and monocytes are elevated during summer, likely reflecting seasonal respiratory diseases and allergens. Seasonality is more pronounced in older patients and varies by gender. Our findings suggest that recognizing these patterns and adjusting reference intervals for seasonality would allow healthcare providers to enhance diagnostic precision, tailor care, and potentially improve patient outcomes.

Exploring the Limits of Artificial Intelligence for Referencing Scientific Articles.

OBJECTIVE: To evaluate the reliability of three artificial intelligence (AI) chatbots (ChatGPT, Google Bard, and Chatsonic) in generating accurate references from existing obstetric literature. STUDY DESIGN: Between mid-March and late April 2023, ChatGPT, Google Bard, and Chatsonic were prompted to provide references for specific obstetrical randomized controlled trials (RCTs) published in 2020. RCTs were considered for inclusion if they were mentioned in a previous article that primarily evaluated RCTs published by the top medical and obstetrics and gynecology journals with the highest impact factors in 2020 as well as RCTs published in a new journal focused on publishing obstetric RCTs. The selection of the three AI models was based on their popularity, performance in natural language processing, and public availability. Data collection involved prompting the AI chatbots to provide references according to a standardized protocol. The primary evaluation metric was the accuracy of each AI model in correctly citing references, including authors, publication title, journal name, and digital object identifier (DOI). Statistical analysis was performed using a permutation test to compare the performance of the AI models. RESULTS: Among the 44 RCTs analyzed, Google Bard demonstrated the highest accuracy, correctly citing 13.6% of the requested RCTs, whereas ChatGPT and Chatsonic exhibited lower accuracy rates of 2.4 and 0%, respectively. Google Bard often substantially outperformed Chatsonic and ChatGPT in correctly citing the studied reference components. The majority of references from all AI models studied were noted to provide DOIs for unrelated studies or DOIs that do not exist. CONCLUSION: To ensure the reliability of scientific information being disseminated, authors must exercise caution when utilizing AI for scientific writing and literature search. However, despite their limitations, collaborative partnerships between AI systems and researchers have the potential to drive synergistic advancements, leading to improved patient care and outcomes. KEY POINTS: · AI chatbots often cite scientific articles incorrectly.. · AI chatbots can create false references.. · Responsible AI use in research is vital..

Hierarchical models that address measurement error are needed to evaluate the correlation between treatment effect and control group event rate.

OBJECTIVE: To apply a hierarchical model (HM) that addresses measurement error in regression of the treatment effect on the control group event rate (CR). We compare HM to weighted linear regression (WLR) which is subject to measurement error and mathematical coupling. STUDY DESIGN AND SETTING: We reviewed published hierarchical models that address measurement error and implemented a Bayesian version in open-source code to facilitate adoption by meta-analysts. We compared WLR and HM across a very large convenience sample of meta-analyses published in the Cochrane Database of Systematic Reviews. RESULTS: We applied both approaches (WLR and a HM that addresses measurement error) to 3,193 meta-analyses that included 33,071 studies (average 10.28 studies per meta-analysis). A statistically significant slope suggesting an association between the treatment effect and CR was demonstrated with both approaches in 568 (17.19%) meta-analyses, with neither approach in 2,036 (63.77%) meta-analyses, only with WLS in 229 (7.17%) and only with HM in 360 (11.28%) meta-analyses. The majority of slopes were negative (WLR 85%, HM 83%). In the majority of cases, HM had wider confidence intervals (72.53%) and slopes farther from the null (64.77%). CONCLUSION: Approximately 28% of meta-analyses demonstrate a significant association between the treatment effect and CR when HM is used to address measurement error. User-friendly open-source code is provided to meta-analysts interested in exploring this association.

Influence of lack of blinding on the estimation of medication-related harms: a retrospective cohort study of randomized controlled trials.

BACKGROUND: Empirical evidence suggests that lack of blinding may be associated with biased estimates of treatment benefit in randomized controlled trials, but the influence on medication-related harms is not well-recognized. We aimed to investigate the association between blinding and clinical trial estimates of medication-related harms. METHODS: We searched PubMed from January 1, 2015, till January 1, 2020, for systematic reviews with meta-analyses of medication-related harms. Eligible meta-analyses must have contained trials both with and without blinding. Potential covariates that may confound effect estimates were addressed by restricting trials within the comparison or by hierarchical analysis of harmonized groups of meta-analyses (therefore harmonizing drug type, control, dosage, and registration status) across eligible meta-analyses. The weighted hierarchical linear regression was then used to estimate the differences in harm estimates (odds ratio, OR) between trials that lacked blinding and those that were blinded. The results were reported as the ratio of OR (ROR) with its 95% confidence interval (CI). RESULTS: We identified 629 meta-analyses of harms with 10,069 trials. We estimated a weighted average ROR of 0.68 (95% CI: 0.53 to 0.88, P < 0.01) among 82 trials in 20 meta-analyses where blinding of participants was lacking. With regard to lack of blinding of healthcare providers or outcomes assessors, the RORs were 0.68 (95% CI: 0.53 to 0.87, P < 0.01 from 81 trials in 22 meta-analyses) and 1.00 (95% CI: 0.94 to 1.07, P = 0.94 from 858 trials among 155 meta-analyses) respectively. Sensitivity analyses indicate that these findings are applicable to both objective and subjective outcomes. CONCLUSIONS: Lack of blinding of participants and health care providers in randomized controlled trials may underestimate medication-related harms. Adequate blinding in randomized trials, when feasible, may help safeguard against potential bias in estimating the effects of harms.

Comparisons of various estimates of the I2 statistic for quantifying between-study heterogeneity in meta-analysis.

Assessing heterogeneity between studies is a critical step in determining whether studies can be combined and whether the synthesized results are reliable. The I2 statistic has been a popular measure for quantifying heterogeneity, but its usage has been challenged from various perspectives in recent years. In particular, it should not be considered an absolute measure of heterogeneity, and it could be subject to large uncertainties. As such, when using I2 to interpret the extent of heterogeneity, it is essential to account for its interval estimate. Various point and interval estimators exist for I2. This article summarizes these estimators. In addition, we performed a simulation study under different scenarios to investigate preferable point and interval estimates of I2. We found that the Sidik-Jonkman method gave precise point estimates for I2 when the between-study variance was large, while in other cases, the DerSimonian-Laird method was suggested to estimate I2. When the effect measure was the mean difference or the standardized mean difference, the Q-profile method, the Biggerstaff-Jackson method, or the Jackson method was suggested to calculate the interval estimate for I2 due to reasonable interval length and more reliable coverage probabilities than various alternatives. For the same reason, the Kulinskaya-Dollinger method was recommended to calculate the interval estimate for I2 when the effect measure was the log odds ratio.

Single-balloon catheter with concomitant vaginal misoprostol is the most effective strategy for labor induction: a meta-review with network meta-analysis.

OBJECTIVE: Several systematic reviews and meta-analyses have been conducted to summarize the evidence for the efficacy of various labor induction agents. However, the most effective agents or strategies have not been conclusively determined. We aimed to perform a meta-review and network meta-analysis of published systematic reviews to determine the efficacy and safety of currently employed pharmacologic, mechanical, and combined methods of labor induction. DATA SOURCES: With the assistance of an experienced medical librarian, we performed a systematic search of the literature using PubMed, EMBASE, and the Cochrane Central Register of Control Trials. We systematically searched electronic databases from inception to May 31, 2021. STUDY ELIGIBILITY CRITERIA: We considered systematic reviews and meta-analyses of randomized controlled trials comparing different agents or methods for inpatient labor induction. METHODS: We conducted a frequentist random-effects network meta-analysis employing data from randomized controlled trials of published systematic reviews. We performed direct pairwise meta-analyses to compare the efficacy of the various labor induction agents and placebo or no treatment. We performed ranking to determine the best treatment using the surface under the cumulative ranking curve. The main outcomes assessed were cesarean delivery, vaginal delivery within 24 hours, operative vaginal delivery, hyperstimulation, neonatal intensive care unit admissions, and Apgar scores of <7 at 5 minutes of birth. RESULTS: We included 11 systematic reviews and extracted data from 207 randomized controlled trials with a total of 40,854 participants. When assessing the efficacy of all agents and methods, the combination of a single-balloon catheter with misoprostol was the most effective in reducing the odds of cesarean delivery and vaginal birth >24 hours (surface under the cumulative ranking curve of 0.9 for each). Among the pharmacologic agents, low-dose vaginal misoprostol was the most effective in reducing the odds of cesarean delivery, whereas high-dose vaginal misoprostol was the most effective in achieving vaginal delivery within 24 hours (surface under the cumulative ranking curve of 0.9 for each). Single-balloon catheter (surface under the cumulative ranking curve of 0.8) and double-balloon catheter (surface under the cumulative ranking curve of 0.9) were the most effective in reducing the odds of operative vaginal delivery and hyperstimulation. Buccal or sublingual misoprostol (surface under the cumulative ranking curve of 0.9) and the combination of single-balloon catheter and misoprostol (surface under the cumulative ranking curve of 0.9) most effectively reduced the odds of abnormal Apgar scores and neonatal intensive care unit admissions. CONCLUSION: The combination of a single-balloon catheter with misoprostol was the most effective method in reducing the odds for cesarean delivery and prolonged time to vaginal delivery. This method was associated with a reduction in admissions to the neonatal intensive care unit.

Outpatient cervical ripening and labor induction with low-dose vaginal misoprostol reduces the interval to delivery: a systematic review and network meta-analysis.

OBJECTIVE: Several systematic reviews and meta-analyses have summarized the evidence on the efficacy and safety of various outpatient cervical ripening methods. However, the method with the highest efficacy and safety profile has not been determined conclusively. We performed a systematic review and network meta-analysis of published randomized controlled trials to assess the efficacy and safety of cervical ripening methods currently employed in the outpatient setting. DATA SOURCES: With the assistance of an experienced medical librarian, we performed a systematic search of the literature using MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, and ClinicalTrials.gov. We systematically searched electronic databases from inception to January 14, 2020. STUDY ELIGIBILITY CRITERIA: We considered randomized controlled trials comparing a variety of methods for outpatient cervical ripening. METHODS: We conducted a frequentist random effects network meta-analysis employing data from randomized controlled trials. We performed a direct, pairwise meta-analysis to compare the efficacy of various outpatient cervical ripening methods, including placebo. We employed ranking strategies to determine the most efficacious method using the surface under the cumulative ranking curve; a higher surface under the cumulative ranking curve value implied a more efficacious method. We assessed the following outcomes: time from intervention to delivery, cesarean delivery rates, changes in the Bishop score, need for additional ripening methods, incidence of Apgar scores <7 at 5 minutes, and uterine hyperstimulation. RESULTS: We included data from 42 randomized controlled trials including 6093 participants. When assessing the efficacy of all methods, 25 µg vaginal misoprostol was the most efficacious in reducing the time from intervention to delivery (surface under the cumulative ranking curve of 1.0) without increasing the odds of cesarean delivery, the need for additional ripening methods, the incidence of a low Apgar score, or uterine hyperstimulation. Acupressure (surface under the cumulative ranking curve of 0.3) and primrose oil (surface under the cumulative ranking curve of 0.2) were the least effective methods in reducing the time to delivery interval. Among effective methods, 50 mg oral mifepristone was associated with the lowest odds of cesarean delivery (surface under the cumulative ranking curve of 0.9). CONCLUSION: When balancing efficacy and safety, vaginal misoprostol 25 µg represents the best method for outpatient cervical ripening.

Water birth: a systematic review and meta-analysis of maternal and neonatal outcomes.

OBJECTIVE: This systematic review and meta-analysis aimed to conduct a thorough and contemporary assessment of maternal and neonatal outcomes associated with water birth in comparison with land-based birth. DATA SOURCES: We conducted a comprehensive search of PubMed, EMBASE, CINAHL, and gray literature sources, from inception to February 28, 2023. STUDY ELIGIBILITY CRITERIA: We included randomized and nonrandomized studies that assessed maternal and neonatal outcomes in patients who delivered either conventionally or while submerged in water. METHODS: Pooled unadjusted odds ratios with 95% confidence intervals were calculated using a random-effects model (restricted maximum likelihood method). We assessed the 95% prediction intervals to estimate the likely range of future study results. To evaluate the robustness of the results, we calculated fragility indices. Maternal infection was designated as the primary outcome, whereas postpartum hemorrhage, perineal lacerations, obstetrical anal sphincter injury, umbilical cord avulsion, low Apgar scores, neonatal aspiration requiring resuscitation, neonatal infection, neonatal mortality within 30 days of birth, and neonatal intensive care unit admission were considered secondary outcomes. RESULTS: Of the 20,642 articles identified, 52 were included in the meta-analyses. Based on data from observational studies, water birth was not associated with increased probability of maternal infection compared with land birth (10 articles, 113,395 pregnancies; odds ratio, 0.93; 95% confidence interval, 0.76-1.14). Patients undergoing water birth had decreased odds of postpartum hemorrhage (21 articles, 149,732 pregnancies; odds ratio, 0.80; 95% confidence interval, 0.68-0.94). Neonates delivered while submerged in water had increased odds of cord avulsion (10 articles, 91,504 pregnancies; odds ratio, 1.75; 95% confidence interval, 1.38-2.24) and decreased odds of low Apgar scores (21 articles, 165,917 pregnancies; odds ratio, 0.69; 95% confidence interval, 0.58-0.82), neonatal infection (15 articles, 53,635 pregnancies; odds ratio, 0.64; 95% confidence interval, 0.42-0.97), neonatal aspiration requiring resuscitation (19 articles, 181,001 pregnancies; odds ratio, 0.60; 95% confidence interval, 0.43-0.84), and neonatal intensive care unit admission (30 articles, 287,698 pregnancies; odds ratio, 0.56; 95% confidence interval, 0.45-0.70). CONCLUSION: When compared with land birth, water birth does not appear to increase the risk of most maternal and neonatal complications. Like any other delivery method, water birth has its unique considerations and potential risks, which health care providers and expectant parents should evaluate thoroughly. However, with proper precautions in place, water birth can be a reasonable choice for mothers and newborns, in facilities equipped to conduct water births safely.

Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses.

Meta-analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be carefully considered. A critical problem in meta-analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid conclusions and affect the robustness of decision-making. Outliers may be caused by several factors such as study selection criteria, low study quality, small-study effects, and so on. Although outlier detection is well-studied in the statistical community, limited attention has been paid to meta-analysis. The conventional outlier detection method in meta-analysis is based on a leave-one-study-out procedure. However, when calculating a potentially outlying study's deviation, other outliers could substantially impact its result. This article proposes an iterative method to detect potential outliers, which reduces such an impact that could confound the detection. Furthermore, we adopt bagging to provide valid inference for sensitivity analyses of excluding outliers. Based on simulation studies, the proposed iterative method yields smaller bias and heterogeneity after performing a sensitivity analysis to remove the identified outliers. It also provides higher accuracy on outlier detection. Two case studies are used to illustrate the proposed method's real-world performance.

Effects of occupational exposure to dust, gas, vapor and fumes on chronic bronchitis and lung function.

Background: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of mortality worldwide, and therefore the identification of the modifiable risk factors [such as exposure to vapors, gases, dust and fumes (VGDF)] for accelerate disease progression has important significance. Methods: We conducted COPD surveillance in six cities of southern China between 2014 and 2019. We recorded the diagnosis of chronic bronchitis, respiratory symptoms, occupational exposure to VGDF and other covariates by using a structured questionnaire. Logistic regression and multivariate linear regression model were adopted for analysis. We performed sensitivity analyses based on two methods of propensity score (PS) methods to evaluate the robustness of our results. Results: A total of 7,418 participants were included. Cough [odds ratios (ORs): 1.60, 95% confidence interval (CI): 1.22 to 2.08] and phlegm (OR: 1.49, 95% CI: 1.19 to 1.85) correlated significantly with exposure to dust. There was an increased risk of cough (OR: 1.53, 95% CI: 1.11 to 2.07) for occupational exposure to gas/vapor/fume. Dual exposure to dust and gas/vapor/fume was associated with a significantly increased risk of chronic bronchitis (OR: 1.74, 95% CI: 1.20 to 2.52), cough (OR: 1.43, 95% CI: 1.15 to 1.79) and phlegm (OR: 1.49, 95% CI: 1.24 to 1.79). In 5,249 participants with complete data of spirometry, gas/vapor/fume was associated with a decreased ratio of forced expiratory volume in one second and forced vital capacity (FEV1/FVC) (ß: -1.05, 95% CI: -1.85 to -0.26) and maximal mid-expiratory flow (MMEF) (ß: -0.15, 95% CI: -0.23 to -0.07). Dual exposure to dust and gas/vapor/fume was significantly associated with decreased FEV1/FVC (ß: -0.74, 95% CI: -1.28 to -0.20) and MMEF (ß: -0.06, 95% CI: -0.12 to -0.01). Results of sensitivity analysis were not materially changed. Conclusions: VGDF exposure is associated with chronic bronchitis, respiratory symptoms and decreased lung function, suggesting that VGDF contributes to the pathogenesis and progression of COPD.

Comparisons of the mean differences and standardized mean differences for continuous outcome measures on the same scale.

When conducting systematic reviews and meta-analyses of continuous outcomes, the mean differences (MDs) and standardized mean differences (SMDs) are 2 commonly used choices for effect measures. The SMDs are motivated by scenarios where studies collected in a systematic review do not report the continuous measures on the same scale. The standardization process transfers the MDs to be unit-free measures that can be synthesized across studies. As such, some evidence synthesis researchers tend to prefer the SMD over the MD. However, other researchers have concerns about the interpretability of the SMD. The standardization process could also yield additional heterogeneity between studies. In this paper, we use simulation studies to illustrate that, in a scenario where the continuous measures are on the same scale, the SMD could have considerably poorer performance compared with the MD in some cases. The simulations compare the MD and SMD in various settings, including cases where the normality assumption of continuous measures does not hold. We conclude that although the SMD remains useful for evidence synthesis of continuous measures on different scales, the SMD could have substantially greater biases, greater mean squared errors, and lower coverage probabilities of CIs than the MD. The MD is generally more robust to the violation of the normality assumption for continuous measures. In scenarios where continuous measures are inherently comparable or can be transformed to a common scale, the MD is the preferred choice for an effect measure.

Temporal trends and projection of blindness and vision loss prevalence in older adults in BRICS countries.

BACKGROUND: Blindness and vision loss (BVL) is a major global health issue affecting older adults, but its burden in transition countries has received limited attention. Therefore, we aimed to assess the trends in the burden of BVL among older adults between 1990 and 2019 across Brazil, Russia, India, China, and South Africa (BRICS), and predict the burden by 2040. METHODS: Data on BVL and its related causes were obtained from the Global Burden of Disease 2019 study. We investigated the temporal trends by calculating the average annual percentage change using joinpoint regression analysis. Subsequently, we performed Bayesian age-period-cohort modeling to estimate the burden of BVL and its related causes by 2040. RESULTS: Most BRICS countries experienced a significant decline (p < 0.05) in age-standardized prevalence rates, and the decreasing trends tend to continue. However, by 2040, the number of BVL cases is expected to increase by approximately 50% across BRICS, with an estimated approximately 192, 170, 25, 17, and 7 million cases in China, India, Russia, Brazil, and South Africa, respectively. The related ranks of BVL causes are also estimated to change in the future, particularly in India. CONCLUSIONS: The different burdens and trends of BVL across BRICS reflected the different stages of population health transition. Effective eye disease prevention requires appropriate public health interventions. Developing effective health policies and services for older adults is urgently needed in BRICS countries.

Confidence Score: A Data-Driven Measure for Inclusive Systematic Reviews Considering Unpublished Preprints.

OBJECTIVES: COVID-19, since its emergence in December 2019, has globally impacted research. Over 360,000 COVID-19-related manuscripts have been published on PubMed and preprint servers like medRxiv and bioRxiv, with preprints comprising about 15% of all manuscripts. Yet, the role and impact of preprints on COVID-19 research and evidence synthesis remain uncertain. MATERIALS AND METHODS: We propose a novel data-driven method for assigning weights to individual preprints in systematic reviews and meta-analyses. This weight termed the "confidence score" is obtained using the survival cure model, also known as the survival mixture model, which takes into account the time elapsed between posting and publication of a preprint, as well as metadata such as the number of first two-week citations, sample size, and study type. RESULTS: Using 146 preprints on COVID-19 therapeutics posted from the beginning of the pandemic through April 30, 2021, we validated the confidence scores, showing an area under the curve (AUC) of 0.95 (95% CI: 0.92, 0.98). Through a use case on the effectiveness of hydroxychloroquine, we demonstrated how these scores can be incorporated practically into meta-analyses to properly weigh preprints. DISCUSSION: It is important to note that our method does not aim to replace existing measures of study quality but rather serves as a supplementary measure that overcomes some limitations of current approaches. CONCLUSION: Our proposed confidence score has the potential to improve systematic reviews of evidence related to COVID-19 and other clinical conditions by providing a data-driven approach to including unpublished manuscripts.

Therapeutic targeting of CPSF3-dependent transcriptional termination in ovarian cancer.

Transcriptional dysregulation is a recurring pathogenic hallmark and an emerging therapeutic vulnerability in ovarian cancer. Here, we demonstrated that ovarian cancer exhibited a unique dependency on the regulatory machinery of transcriptional termination, particularly, cleavage and polyadenylation specificity factor (CPSF) complex. Genetic abrogation of multiple CPSF subunits substantially hampered neoplastic cell viability, and we presented evidence that their indispensable roles converged on the endonuclease CPSF3. Mechanistically, CPSF perturbation resulted in lengthened 3'-untranslated regions, diminished intronic polyadenylation and widespread transcriptional readthrough, and consequently suppressed oncogenic pathways. Furthermore, we reported the development of specific CPSF3 inhibitors building upon the benzoxaborole scaffold, which exerted potent antitumor activity. Notably, CPSF3 blockade effectively exacerbated genomic instability by down-regulating DNA damage repair genes and thus acted in synergy with poly(adenosine 5'-diphosphate-ribose) polymerase inhibition. These findings establish CPSF3-dependent transcriptional termination as an exploitable driving mechanism of ovarian cancer and provide a promising class of boron-containing compounds for targeting transcription-addicted human malignancies.

Nitrogen removal from dewatering liquid of landfill sludge by partial nitrification and denitrification.

Two pilot-scale two-stage anoxic/oxic membrane bioreactors were operated at different dissolved oxygen (DO) levels to evaluate nitrogen removal performances for treating landfill sludge dewatering liquid. Under either high (5.0-6.0 mg/L) or conventional DO (2.0-3.0 mg/L) conditions, partial nitrification (PN)-denitrification was both achieved, and high-concentration free ammonia (FA) ensured stable PN. The high DO system exhibited higher nitrite accumulation (98.5 %) and nitrogen removal (98.0 %), and its nitrogen removal was mainly ascribed to PN-denitrification (53.8 %). Kinetic inhibition tests and microbial sequencing results demonstrated that high DO condition improved the abundance and ability of ammonia-oxidizing bacteria (AOB) rather than nitrite-oxidizing bacteria under the FA inhibition. Pseudomonas, Thauera, and Soehngenia were characteristic genus in the high DO system, and Nitrosomonas was only AOB. Metagenomic analysis confirmed the important role of PN on nitrogen removal in high DO system. This provides valuable references for the efficient and economic treatment of ammonia-rich wastewater.

The comparison of mortality burden between exposure to dry-cold events and wet-cold events: A nationwide study in China.

BACKGROUND: Most previous studies have focused on the health effect of temperature or humidity, and few studies have explored the combined health effects of exposure to temperature and humidity. This study aims to estimate the relationship between humidity-cold events and mortality, and then to compare the mortality burden between exposure to dry-cold events and wet-cold events, and finally to explore whether there was an additive interaction of temperature and humidity on mortality. METHODS: In the study, Daily mortality data during 2006-2017 were collected from Centers for Disease Control and Prevention in China, and daily mean temperature and daily mean relative humidity data from 698 weather stations in China were obtained from the China Meteorological Data Sharing Service system. We first employed time-series design with a distributed lag nonlinear model and a multivariate meta-analysis model to examine the association between humidity-cold events with mortality. RESULTS: We found that humidity-cold events significantly increased mortality risk, and the effect of wet-cold events (RR:1.24, 95%CI:1.20,1.29) was higher than that of dry-cold events (RR:1.14, 95%CI:1.10,1.18). Dry-cold events and wet-cold events accounted for 2.41 % and 2.99 % excess deaths, respectively with higher burden for the elderly ≥85 years old, Central China and CVD. In addition, there is a synergistic additive interaction between low temperature and high humidity in winter. CONCLUSION: This study showed that humidity-cold events significantly increased mortality risk, and the effect of wet-cold events was higher than that of dry-cold events.

Bayesian hierarchical models incorporating study-level covariates for multivariate meta-analysis of diagnostic tests without a gold standard with application to COVID-19.

When evaluating a diagnostic test, it is common that a gold standard may not be available. One example is the diagnosis of SARS-CoV-2 infection using saliva sampling or nasopharyngeal swabs. Without a gold standard, a pragmatic approach is to postulate a "reference standard," defined as positive if either test is positive, or negative if both are negative. However, this pragmatic approach may overestimate sensitivities because subjects infected with SARS-CoV-2 may still have double-negative test results even when both tests exhibit perfect specificity. To address this limitation, we propose a Bayesian hierarchical model for simultaneously estimating sensitivity, specificity, and disease prevalence in the absence of a gold standard. The proposed model allows adjusting for study-level covariates. We evaluate the model performance using an example based on a recently published meta-analysis on the diagnosis of SARS-CoV-2 infection and extensive simulations. Compared with the pragmatic reference standard approach, we demonstrate that the proposed Bayesian method provides a more accurate evaluation of prevalence, specificity, and sensitivity in a meta-analytic framework.

GRADE GUIDANCE 38: Updated guidance for rating up certainty of evidence due to a dose-response gradient.

INTRODUCTION: This updated guidance from the Grading of Recommendations Assessment, Development, and Evaluation addresses rating up certainty of evidence due to a dose-response gradient (DRG) observed in synthesis of intervention and exposure studies. STUDY DESIGN AND SETTING: This guidance was developed using iterative discussions and consensus in multiple meetings and was presented to attendees of the Grading of Recommendations Assessment, Development, and Evaluation Working Group meeting for feedback in November 2022 and for final approval in May 2023. RESULTS: The guidance consists of two steps. The first is to determine whether the DRG is credible. We describe five items for assessing credibility: a) is DRG identified using a proper analytical approach; b) is confounding the cause of the DRG; c) is there serious concern about ecological bias; d) is the DRG consistent across studies; and e) is there indirect evidence supporting the DRG. The first two of these items are the most critical. If the DRG was judged to be credible, then the second step is to apply the DRG domain and consider rating up, but only by one level due to the concern about residual confounding. CONCLUSION: Systematic review authors should only rate up certainty in evidence when a DRG is deemed credible.