Outcome reporting bias (ORB) is recognized as a threat to the validity of both pairwise and network meta‐analysis (NMA). In recent years, multivariate meta‐analytic methods have been proposed to ...reduce the impact of ORB in the pairwise setting. These methods have shown that multivariate meta‐analysis can reduce bias and increase efficiency of pooled effect sizes. However, it is unknown whether multivariate NMA (MNMA) can similarly reduce the impact of ORB. Additionally, it is quite challenging to implement MNMA due to the fact that correlation between treatments and outcomes must be modeled; thus, the dimension of the covariance matrix and number of components to estimate grows quickly with the number of treatments and number of outcomes. To determine whether MNMA can reduce the effects of ORB on pooled treatment effect sizes, we present an extensive simulation study of Bayesian MNMA. Via simulation studies, we show that MNMA reduces the bias of pooled effect sizes under a variety of outcome missingness scenarios, including missing at random and missing not at random. Further, MNMA improves the precision of estimates, producing narrower credible intervals. We demonstrate the applicability of the approach via application of MNMA to a multi‐treatment systematic review of randomized controlled trials of anti‐depressants for the treatment of depression in older adults.
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Abstract
Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. ...Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not).
A longitudinal model shows that expected antibody response after COVID-19 infection increases for 100 days postinfection, and predicts individuals may remain antibody positive beyond 500 days, depending on age, body mass index, smoking or vaping use, and disease severity.
Abstract
Background
Oropharyngeal cancers associated with high-risk human papillomavirus (HR-HPV) infection are increasing in the United States, especially among men. We evaluated the prevalence and ...predictors of concurrent (genital and oral) and concordant (same-type) HR-HPV infections in the United States.
Methods
We used the National Health and Nutrition Examination Survey from 2009 to 2016. Predictors were assessed via multivariable logistic regression.
Results
Among 10 334 respondents, 172 (2.1%) had concurrent infections (109 3.5% men and 63 0.76% women. Ninety-three (1.0%) had concordant infections (54 1.6% men and 39 0.5% women). Predictors of concurrence in men included the following: no longer married versus married (odds ratio OR, 2.3; 95% confidence interval CI, 1.3–4.9), living with a partner versus married (3.0; 1.2–7.5), and having 2–5 lifetime oral sex partners (3.0; 1.2–7.5). In women they included the following: no longer married versus married (3.6; 1.3–10.3), ≥2 recent sex partners (4.6; 1.4–15.6 for 2–5 partners and 3.9; 1.1–14.3 for ≥6 partners), and marijuana use (2.2; 1.0–4.5). The predictor of concordance in men and women was no longer married versus married (3.5; 1.2–9.9 in men and 3.2; 1.1–9.4 in women).
Conclusions
Concurrent and concordant HR-HPV infections occur at a high rate, especially among men, and are associated with behavioral factors. This underscores the importance of HPV vaccination, screening, and education in men.
This study found a high prevalence rates of concurrent and concordant high-risk human papillomavirus (HR-HPV) infections, especially among men, adding to the knowledge base for developing new screening and vaccination guidelines for HR-HPV infections, specifically targeting the US male population.
Longitudinal zero-inflated count data arise frequently in substance use research when assessing the effects of behavioral and pharmacological interventions. Zero-inflated count models (e.g. ...zero-inflated Poisson or zero-inflated negative binomial) with random effects have been developed to analyze this type of data. In random effects zero-inflated count models, the random effects covariance matrix is typically assumed to be homogeneous (constant across subjects). However, in many situations this matrix may be heterogeneous (differ by measured covariates). In this paper, we extend zero-inflated count models to account for random effects heterogeneity by modeling their variance as a function of covariates. We show via simulation that ignoring intervention and covariate-specific heterogeneity can produce biased estimates of covariate and random effect estimates. Moreover, those biased estimates can be rectified by correctly modeling the random effects covariance structure. The methodological development is motivated by and applied to the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study, the largest clinical trial of alcohol dependence performed in United States with 1383 individuals.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Carbapenem-resistant Enterobacterales (CRE) pose a serious public health threat and spread rapidly between healthcare facilities (HCFs) during interfacility patient movement. We examined patterns of ...transmission of CRE associated with network clustering and positions during patient interfacility transfer.
A retrospective cohort study was conducted in the Greater Houston region ofTexas, , and social network analysis was performed by constructing facility-to-facility patient transfer network using CRE surveillance data. The network method (community detection algorithm) was used to detect clustering patterns of CRE in the network. In addition, network measures of centrality and local connectivity (clustering coefficient) were computed for each healthcare facility. Zero-inflated negative binomial regression analysis was applied to test the association between network measures and facility-specific incidence rate of CRE.
A network of 268 healthcare facilities was identified, in which 10 acute-care hospitals (ACHs) alone accounted for 63% of identified CRE cases. Transmission of New Delhi metallo-β-lactamase-producing CRE occurred in 3 clusters, yet all cases were traced to patients who had had medical care abroad. The incidence rate of CRE attributed to ACHs was >4-fold (adjusted rate ratio, 4.5; 95% confidence interval CI, 3.02-6.72) higher than that of long-term care facilities. Each additional patient shared with another HCF conferred a 3% (95% CI, 2%-4%) increase in the incidence rate of CRE at that HCF.
The incidence rates of CRE at a given HCF was predicted by the healthcare network metrics. Increased surveillance and selective targeting of high-risk facilities are warranted.
Based on continuous monitoring of the pressure reactivity index (PRx), the authors defined individualized intracranial pressure (ICP) thresholds by graphing the relationship between ICP and PRx. ...These investigators hypothesized that an "ICP dose" based on individually assessed ICP thresholds would correlate more closely with the 6-month outcome when compared with ICP doses derived by the recommended universal thresholds of 20 and 25 mm Hg.
This study was a retrospective analysis of prospectively collected data from 327 patients with severe traumatic brain injury.
Individualized thresholds were visually identified from graphs of PRx versus ICP; PRx > 0.2 was the cutoff. Intracranial pressure doses were then computed as the cumulative area under the curve above the defined thresholds in graphing ICP versus time. The term "Dose 20" (D20) was used to refer to an ICP threshold of 20 mm Hg; the markers D25 and DPRx were calculated similarly. Separate logistic regression models were fit with death as the outcome and each dose as the predictor, both alone and adjusted for covariates. The discriminative ability of each dose for mortality was assessed by receiver operating characteristic AUC analysis in which 5-fold cross-validation was used. A clearly identifiable PRx-based threshold was possible in 224 patients (68%). The DPRx (AUC 0.81, 95% CI 0.74-0.87) was found to have the highest area under the curve (AUC) over both D20 (0.75, 95% CI 0.68-0.81) and D25 (0.77, 95% CI 0.70-0.83); in the cross-validation model, DPRx remained the best discriminator of mortality (DPRx: AUC 0.77 95% CI 0.68-0.89; D20: 0.72 95% CI 0.66-0.81; and D25: 0.65 95% CI 0.56-0.73).
The authors explored the importance of different ICP thresholds for outcome by calculating patient-specific ICP doses based on the continuous monitoring of cerebrovascular pressure reactivity. They found that these individualized doses of intracranial hypertension were stronger predictors of death than doses derived from the universal thresholds of 20 and 25 mm Hg. The PRx could offer a method that can be directed toward individualizing the ICP threshold.
Studies indicate that individuals with chronic conditions and specific baseline characteristics may not mount a robust humoral antibody response to SARS-CoV-2 vaccines. In this paper, we used data ...from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a longitudinal state-wide seroprevalence program that has enrolled more than 90,000 participants, to evaluate the role of chronic diseases as the potential risk factors of non-response to SARS-CoV-2 vaccines in a large epidemiologic cohort.
A participant needed to complete an online survey and a blood draw to test for SARS-CoV-2 circulating plasma antibodies at four-time points spaced at least three months apart. Chronic disease predictors of vaccine non-response are evaluated using logistic regression with non-response as the outcome and each chronic disease + age as the predictors.
As of April 24, 2023, 18,240 participants met the inclusion criteria; 0.58% (N = 105) of these are non-responders. Adjusting for age, our results show that participants with self-reported immunocompromised status, kidney disease, cancer, and "other" non-specified comorbidity were 15.43, 5.11, 2.59, and 3.13 times more likely to fail to mount a complete response to a vaccine, respectively. Furthermore, having two or more chronic diseases doubled the prevalence of non-response.
Consistent with smaller targeted studies, a large epidemiologic cohort bears the same conclusion and demonstrates immunocompromised, cancer, kidney disease, and the number of diseases are associated with vaccine non-response. This study suggests that those individuals, with chronic diseases with the potential to affect their immune system response, may need increased doses or repeated doses of COVID-19 vaccines to develop a protective antibody level.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Accurate estimates of natural and/or vaccine-induced antibodies to SARS-CoV-2 are difficult to obtain. Although model-based estimates of seroprevalence have been proposed, they require inputting ...unknown parameters including viral reproduction number, longevity of immune response, and other dynamic factors. In contrast to a model-based approach, the current study presents a data-driven detailed statistical procedure for estimating total seroprevalence (defined as antibodies from natural infection or from full vaccination) in a region using prospectively collected serological data and state-level vaccination data. Specifically, we conducted a longitudinal statewide serological survey with 88,605 participants 5 years or older with 3 prospective blood draws beginning September 30, 2020. Along with state vaccination data, as of October 31, 2021, the estimated percentage of those 5 years or older with naturally occurring antibodies to SARS-CoV-2 in Texas is 35.0% (95% CI = (33.1%, 36.9%)). This is 3x higher than, state-confirmed COVID-19 cases (11.83%) for all ages. The percentage with naturally occurring or vaccine-induced antibodies (total seroprevalence) is 77.42%. This methodology is integral to pandemic preparedness as accurate estimates of seroprevalence can inform policy-making decisions relevant to SARS-CoV-2.
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The antioxidant N-acetylcysteine is being increasingly investigated as a therapeutic agent in the treatment of substance use disorders (SUDs). This study explored the efficacy of N-acetylcysteine in ...the treatment of posttraumatic stress disorder (PTSD), which frequently co-occurs with SUD and shares impaired prefrontal cortex regulation of basal ganglia circuitry, in particular at glutamate synapses in the nucleus accumbens.
Veterans with PTSD and SUD per DSM-IV criteria (N = 35) were randomly assigned to receive a double-blind, 8-week course of N-acetylcysteine (2,400 mg/d) or placebo plus cognitive-behavioral therapy for SUD (between March 2013 and April 2014). Primary outcome measures included PTSD symptoms (Clinician-Administered PTSD Scale, PTSD Checklist-Military) and craving (Visual Analog Scale). Substance use and depression were also assessed.
Participants treated with N-acetylcysteine compared to placebo evidenced significant improvements in PTSD symptoms, craving, and depression (β values < -0.33; P values < .05). Substance use was low for both groups, and no significant between-group differences were observed. N-acetylcysteine was well tolerated, and retention was high.
This is the first randomized controlled trial to investigate N-acetylcysteine as a pharmacologic treatment for PTSD and SUD. Although preliminary, the findings provide initial support for the use of N-acetylcysteine in combination with psychotherapy among individuals with co-occurring PTSD and SUD.
ClinicalTrials.gov identifier: NCT02499029.
Many randomized controlled trials report more than one primary outcome. As a result, multivariate meta-analytic methods for the assimilation of treatment effects in systematic reviews of randomized ...controlled trials have received increasing attention in the literature. These methods show promise with respect to bias reduction and efficiency gain compared with univariate meta-analysis. However, most methods for multivariate meta-analysis have focused on pairwise treatment comparisons (i.e. when the number of treatments is 2). Current methods for mixed treatment comparisons meta-analysis (i.e. when the number of treatments is more than 2) have focused on univariate or, very recently, bivariate outcomes. To broaden their application, we propose a framework for mixed treatment comparisons meta-analysis of multivariate (two or more) outcomes where the correlations between multivariate outcomes within and between studies are accounted for through copulas, and the joint modelling of multivariate random effects respectively. We consider a Bayesian hierarchical model using Markov chain Monte Carlo methods for estimation. An important feature of the framework proposed is that it allows for borrowing of information across correlated outcomes. We show via simulation that our approach reduces the effect of outcome reporting bias in a variety of missing outcome scenarios. We apply the method to a systematic review of randomized controlled trials of pharmacological treatments for alcohol dependence, which tends to report multiple outcomes potentially subject to outcome reporting bias.
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BFBNIB, FZAB, GIS, IJS, INZLJ, IZUM, KILJ, NLZOH, NMLJ, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
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