How does pain lead to disability? A systematic review and meta-analysis of mediation studies in people with back and neck pain

Disability is an important outcome from a clinical and public health perspective. However, it is unclear how disability develops in people with low back pain or neck pain. More specifically, the mechanisms by which pain leads to disability are not well understood. Mediation analysis is a way of investigating these mechanisms by examining the extent to which an intermediate variable explains the effect of an exposure on an outcome. This systematic review and meta-analysis aimed to identify and examine the extent to which putative mediators explain the effect of pain on disability in people with low back pain or neck pain. Five electronic databases were searched. We found 12 studies (N = 2961) that examined how pain leads to disability with mediation analysis. Standardized regression coefficients (β) of the indirect and total paths were pooled. We found evidence to show that self-efficacy (β = 0.23, 95% confidence interval [CI] = 0.10 to 0.34), psychological distress (β = 0.10, 95% CI = 0.01 to 0.18), and fear (β = 0.08, 95% CI = 0.01 to 0.14) mediated the relationship between pain and disability, but catastrophizing did not (β = 0.07, 95% CI = −0.06 to 0.19). The methodological quality of these studies was low, and we highlight potential areas for development. Nonetheless, the results suggest that there are significant mediating effects of self-efficacy, psychological distress, and fear, which underpins the direct targeting of these constructs in treatment

Adm Policy Ment Health

With new tools from artificial intelligence and new perspectives on personalizing interventions, we could revolutionize the way mental health services are delivered and achieve major gains in improving the public's mental health. We examine Dr. Bickman's vision around these technological and paradigm changes that would usher in major scientific, workforce training, and societal cultural changes. We argue that additional efforts in research evaluations in implementation have the potential to scale up and adapt existing interventions and scale them out to diverse populations and service systems. The next stage of this work involves testing the effectiveness of personalized interventions that are preferred by the public and integrating these choices into sustainable service systems. We note cautions on the delivery of these programs as automated algorithmic recommendations are heretofore foreign to humans.R34 DA037516/DA/NIDA NIH HHSUnited States/R01 MH124718/MH/NIMH NIH HHSUnited States/R01MH117598/MH/NIMH NIH HHSUnited States/P30DA027828/DA/NIDA NIH HHSUnited States/R01 MH040859/MH/NIMH NIH HHSUnited States/R01 MH117598/MH/NIMH NIH HHSUnited States/P30 DA027828/DA/NIDA NIH HHSUnited States/UL1TR001422/TR/NCATS NIH HHSUnited States/U01 CE002712/CE/NCIPC CDC HHSUnited States/2022-09-09T00:00:00Z32715431PMC946245211892vault:4325

QUANTITATIVE PROTEOMIC ANALYSES OF HUMAN PLASMA: APPLICATION OF MASS SPECTROMETRY FOR THE DISCOVERY OF CLINICAL DELIRIUM BIOMARKERS

The biomarker discovery pipeline is a multi-step endeavor to identify potential diagnostic or prognostic markers of a disease. Although the advent of modern mass spectrometers has revolutionized the initial discovery phase, a significant bottleneck still exists when validating discovered biomarkers. In this doctoral research, I demonstrate that the discovery, verification and validation of biomarkers can all be performed using mass spectrometry and apply the biomarker pipeline to the context of clinical delirium. First, a systematic review of recent literature provided a birds-eye view of untargeted, discovery proteomic attempts for biomarkers of delirium in the geriatric population. Here, a comprehensive search from five databases yielded 1172 publications, from which eight peer-reviewed studies met our defined inclusion criteria. Despite the paucity of published studies that applied systems- biology approaches for biomarker discovery on the subject, lessons learned and insights from this review was instrumental in the study designing and proteomics analyses of plasma sample in our cohort. We then performed a targeted study on four biomarkers for their potential mediation role in the occurrence of delirium after high-dose intra-operative oxygen treatment. Although S100B calcium binding protein (S100B), gamma enolase (ENO2), chitinase-3-like protein 1 (CHI3L1) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) have well-documented associations with delirium, we did not find any such associations in our cohort. Of note, this study demonstrates that the use of targeted approaches for the purposes of biomarker discovery, rather than an untargeted, systems-biology approach, is unavoidably biased and may lead to misleading conclusions. Lastly, we applied lessons learned and comprehensively profiled the plasma samples of delirium cases and non-delirium cases, at both pre- and post-surgical timepoints. We found 16 biomarkers as signatures of cardiopulmonary bypass, and 11 as potential diagnostic candidates of delirium (AuROC = 93%). We validated the discovered biomarkers on the same mass spectrometry platform without the use of traditional affinity-based validation methods. Our discovery of novel biomarkers with no know association with delirium such as serum amyloid A1 (SAA1) and A2 (SAA2), pepsinogen A3 (PEPA3) and cathepsin B (CATB) shed new lights on possible neuronal pathomechanisms

Meta‐analysis and traditional systematic literature reviews—What, why, when, where, and how?

Meta‐analysis is a research method for systematically combining and synthesizing findings from multiple quantitative studies in a research domain. Despite its importance, most literature evaluating meta‐analyses are based on data analysis and statistical discussions. This paper takes a holistic view, comparing meta‐analyses to traditional systematic literature reviews. We described steps of the meta‐analytic process including question definition, data collection, data analysis, and reporting results. For each step, we explain the primary purpose, the tasks required of the meta‐analyst, and recommendations for best practice. Finally, we discuss recent developments in meta‐analytic techniques, which increase its effectiveness in business research

Behavior Change Techniques to Promote Smoking Cessation During Pregnancy: A Theory-Based Meta-Analysis

Despite significant progress, smoking during pregnancy remains one of the leading preventable causes of adverse fetal and maternal health outcomes. Using the current best practice standard of psychosocial counseling, only about one out of every 20 pregnant women quits smoking, and relapse rates are very high. Developing more effective interventions to promote smoking cessation during pregnancy is a critical public health priority that requires a thorough understanding of behavior change and its complex pathways and determinants. As such, the purpose of this three-part study was to conduct the first systematic theory-based evidence synthesis of smoking cessation interventions during pregnancy, and to quantify the effectiveness of specific behavior change techniques and behavioral theories used in these interventions, with the long-term goal of informing the development of more effective interventions to reduce smoking during pregnancy. The first aim was to conduct a meta-analysis to produce quantitative estimates of intervention effect sizes and to identify factors that may explain the observed heterogeneity in intervention effectiveness. A search of six major bibliographic databases for prenatal smoking cessation interventions published between 1995 and 2015 yielded 1,223 unique articles, of which 38 met criteria for inclusion and 34 were randomized controlled trials where the primary outcome was late-pregnancy biochemically-validated smoking cessation and the unit of randomization was the individual. The results of a random effects meta-analysis of the 34 randomized controlled trials of prenatal smoking cessation interventions yielded a significant risk ratio for the primary outcome of late-pregnancy smoking cessation, such that women in the treatment groups were 1.53 times as likely to achieve smoking cessation before giving birth than women in the respective control groups (RR = 1.53; 95% CI: 1.30-1.79). Several study-level variables emerged as potential moderators of intervention effectiveness. Treatment-group participants in contingent rewards interventions were 2.82 times as likely to achieve late-pregnancy smoking abstinence than control group participants. In comparison, treatment-group participants in counseling interventions were 1.3 times as likely to achieve late pregnancy smoking abstinence than their control group counterparts. Intensity level was not associated with effectiveness in this sample. Interventions in this review also yielded promising (significant) results for many secondary outcomes of interest, including additional measures of smoking behavior as well as perinatal outcomes. Specifically, treatment group participants were 1.44 times as likely as control group participants to significantly reduce (by at least 50%) their cigarette consumption, 1.54 times as likely to be smoke free in the early postpartum period, and 1.99 times as likely to be smoke free in the late postpartum period. The results also revealed that smoking cessation interventions reduced the risk of two very common adverse perinatal health outcomes: low birthweight and preterm birth. Specifically, treatment group participants had 73% less risk of delivering a low birthweight or very low birthweight infant and 67% less risk of preterm birth compared to control group participants. The second aim was to evaluate the use of the health behavior theory in intervention design, implementation, and evaluation, and to assess whether the use of theory was associated with intervention effectiveness. Of the 26 published trials that explicitly mentioned theory in the introduction or methods, only nine were based on a single theoretical framework. Five of these studies utilized the learning-based theory of operant conditioning, two studies utilized the transtheoretical/stages of change model, one study used social cognitive theory, and one study used social learning theory. Even among these nine trials, theory was used primarily in a descriptive manner, as opposed to an explanatory or predictive manner. The results of the subgroup analyses and meta-regression models were counter to the hypothesis that use of theory would be positively associated with intervention effectiveness. Scores on two categories of the theory coding scheme (“Was theory tested?” and “Was theory used to tailor or select participants?”) were significantly associated with the primary outcome of late-pregnancy smoking abstinence, but both of the associations were negative, indicating that greater use of theory was associated with a lower likelihood of smoking abstinence during the late-pregnancy period. However, this may reflect the limited use of theory in intervention planning and design among trials included in this meta-analysis, rather than the contribution of theory when it is used optimally. The third aim was to isolate the “active” ingredients in prenatal smoking cessation programs by applying a standardized taxonomy of behavior change techniques to identify the techniques, and then quantifying the effectiveness of each individual technique. We first used Abraham and Michie’s (2008) 26-item taxonomy to identify theory-derived behavior change techniques in published descriptions of intervention content, then performed a meta-regression analysis to determine whether interventions utilizing more techniques were more likely to be effective, and then used subgroup and moderator analyses in order to quantify the effectiveness of each technique. The results revealed that the total number of behavior change techniques used was not associated with late pregnancy smoking abstinence, indicating that more is not necessarily better. Effect sizes were significantly larger for the treatment group than the control group for subsets of interventions that 1) provided information about the link between smoking and health (RR = 1.68; 95% CI: 1.26-2.12); 2) provided information about the negative consequences of smoking (RR = 1.38; 95% CI: 1.08-1.77); 3) prompted the formation of intentions to quit smoking (RR = 1.24; 95% CI: 1.00-1.53); 4) provided instructions (RR = 1.51; 95% CI: 1.21-1.89); 5) prompted specific goal setting (RR = 1.48; 95% CI: 1.17-1.88); 6) provided contingent rewards (RR = 2.82; 95% CI: 2.05-3.88); 7) taught participants to use prompts and/or cues (RR = 1.63; 95% CI: 1.03-2.59); and/or 8) had participants agree to a behavioral contract (RR = 2.14; 95% CI: 1.29-3.56). Several key findings emerged from this study. First, behavior change theory is not being utilized to its full capacity in the development and evaluation of prenatal smoking cessation interventions, with only half of the studies in this review (n = 19) reporting an explicit link between at least one behavior change technique and at least one targeted predictor of behavior change. Secondly, many of the most common behavior change techniques used in prenatal smoking cessation interventions were not associated with better intervention outcomes, nor was the quantity of techniques used associated with effectiveness. Third, the current review identified contingent rewards as the most effective behavior change technique for promoting smoking cessation during pregnancy and into the postpartum period when tangible rewards were no longer offered. While previous meta-analyses have assessed whether or not prenatal smoking cessation interventions were effective, this review expanded on existing findings by using a recently developed taxonomy to identify, isolate, and quantify the effectiveness of individual behavior change techniques used in interventions, as well as applying a coding scheme to evaluate how theory is being used in the literature and whether the use of theory is associated with the effectiveness of interventions. The results provide a framework for evaluating not only if an intervention worked, but also why, how, and under what conditions, marking an important step towards a new set of standards in evidence synthesis and theory-testing in smoking cessation research and beyond

Using the Literature to Identify Confounders

Prior work in causal modeling has focused primarily on learning graph structures and parameters to model data generating processes from observational or experimental data, while the focus of the literature-based discovery paradigm was to identify novel therapeutic hypotheses in publicly available knowledge. The critical contribution of this dissertation is to refashion the literature-based discovery paradigm as a means to populate causal models with relevant covariates to abet causal inference. In particular, this dissertation describes a generalizable framework for mapping from causal propositions in the literature to subgraphs populated by instantiated variables that reflect observational data. The observational data are those derived from electronic health records. The purpose of causal inference is to detect adverse drug event signals. The Principle of the Common Cause is exploited as a heuristic for a defeasible practical logic. The fundamental intuition is that improbable co-occurrences can be “explained away” with reference to a common cause, or confounder. Semantic constraints in literature-based discovery can be leveraged to identify such covariates. Further, the asymmetric semantic constraints of causal propositions map directly to the topology of causal graphs as directed edges. The hypothesis is that causal models conditioned on sets of such covariates will improve upon the performance of purely statistical techniques for detecting adverse drug event signals. By improving upon previous work in purely EHR-based pharmacovigilance, these results establish the utility of this scalable approach to automated causal inference