Periodic Reporting for period 1 - RE-SHIFT (Dismantling, REdialing, personalizing, and implementing task SHIFTing psychosocial interventions to treat and prevent common mental disorders in low-resource settings)
Reporting period: 2022-10-01 to 2024-09-30
RE-SHIFT addresses one of the leading causes of disability worldwide: the ever-increasing prevalence of anxiety and depression. It is known that psychological and social interventions are effective for the treatment of these disorders. In low-resource settings, though, for feasibility reasons, these interventions are not delivered by mental health professionals but by trained lay counselors, so that a wider audience can be reached at a lower cost. However, such interventions are compilations of different, possibly interacting, treatment components, usually delivered in different combinations. And it is not yet known which of these components or combinations thereof are most effective. So how to disentangle the efficacy of individual components and understand which of these works best for whom? This is the research question I am trying to address. To do this, RE-SHIFT is implementing a sophisticated research design that will identify the impact of participant-level prognostic factors and effect modifiers on intervention outcomes. Once the results of RE-SHIFT are automated into software, it will be possible to tailor interventions to the needs and preferences of people with depression and anxiety. This will be done at almost no cost and will hopefully have a global impact.
Dr. Papola conducted a systematic review of randomized controlled trials (RCTs) evaluating the efficacy of task-shared psychosocial interventions using the PICO framework as follows: P (population) = adults diagnosed with common mental disorders (CMD) or experiencing subthreshold psychological distress, seeking first-level/primary care or identified within the community; I (intervention) = any psychosocial intervention provided by non-specialist providers (NSPs), including mentoring programs, cognitive-behavioral therapy and its derivatives, interpersonal therapy, psychoeducation, problem-solving therapy, and others; C (comparison) = no treatment, treatment as usual, waiting list, or another active psychosocial intervention; O (main outcome) = reduction in symptomatology; S (setting) = any context. The systematic literature search, conducted in line with the above PICO criteria, yielded 13,320 references from four bibliometric databases (PubMed, Embase, PsycINFO, CENTRAL). After a rigorous screening process and full-text review, 34 studies were deemed eligible for inclusion. This phase adhered to Cochrane standards to ensure the reliability and quality of the process.
Dr. Papola then contacted the authors of the 34 selected RCTs to obtain (a) the manuals of the psychosocial interventions used in the experimental arms of their trials so that he could qualitatively analyze the manual and create the taxonomy of components (WP1, WP2), and (b) the individual participant data (IPD) from the trials, with the aim of tailoring the results based on participant characteristics (WP3). To analyze the intervention manuals and extract active treatment-specific components from their text, Dr. Papola and his research collaborators conducted multiple rounds of coding, both collaboratively and independently. Dr. Papola and collaborators created an initial list of eligible elements and reviewed it for duplication and redundancy. Each element was then operationalized and assessed by the research team. This process included several pilot testing rounds, during which four researchers independently coded two or more eligible trials to test and refine the existing taxonomy. Interrater reliability (IRR) was assessed using intraclass correlation (ICC). After each round of coding, the team discussed similarities and discrepancies among the coders and adjusted the taxonomy as needed. By the final round, the coding showed a high level of agreement among the researchers (ICC = 0.85–0.96). The outcome of this process was a three-tiered, tree-shaped taxonomy of treatment-specific components. Notably, although other taxonomies of psychological intervention components have been developed previously, this is the first time such an instrument has been designed to support both qualitative and quantitative analyses. To enhance its utility for quantitative purposes, the taxonomy was structured to include three levels of depth and specificity: the 'family' level, the 'component' level, and the 'technique' level. This hierarchical structure allows the level of component analysis to be tailored to the available data. For instance, more granular analyses at the 'technique' level can be conducted when large datasets are available, while broader but still informative analyses can be performed at the 'family' level when data is more limited. An online survey is currently being conducted with the authors of the trials to assess their agreement with the classification of the intervention elements. Therefore, some further adjustments and refinements to the taxonomy are anticipated in the coming months.
Parallel to this, thanks to the collaborative spirit of the trial authors Dr. Papola engaged with, he successfully gathered individual participant data (IPDs) from 30 of the 34 trials in the systematic review. This achievement represents the collection of 10,618 IPDs out of a possible 11,969, covering 89% of the total available data. This compilation forms the most extensive IPD database assembled to date in the field of global mental health. The setup for the main IPD cNMA analysis is currently being implemented.
Dr. Papola then contacted the authors of the 34 selected RCTs to obtain (a) the manuals of the psychosocial interventions used in the experimental arms of their trials so that he could qualitatively analyze the manual and create the taxonomy of components (WP1, WP2), and (b) the individual participant data (IPD) from the trials, with the aim of tailoring the results based on participant characteristics (WP3). To analyze the intervention manuals and extract active treatment-specific components from their text, Dr. Papola and his research collaborators conducted multiple rounds of coding, both collaboratively and independently. Dr. Papola and collaborators created an initial list of eligible elements and reviewed it for duplication and redundancy. Each element was then operationalized and assessed by the research team. This process included several pilot testing rounds, during which four researchers independently coded two or more eligible trials to test and refine the existing taxonomy. Interrater reliability (IRR) was assessed using intraclass correlation (ICC). After each round of coding, the team discussed similarities and discrepancies among the coders and adjusted the taxonomy as needed. By the final round, the coding showed a high level of agreement among the researchers (ICC = 0.85–0.96). The outcome of this process was a three-tiered, tree-shaped taxonomy of treatment-specific components. Notably, although other taxonomies of psychological intervention components have been developed previously, this is the first time such an instrument has been designed to support both qualitative and quantitative analyses. To enhance its utility for quantitative purposes, the taxonomy was structured to include three levels of depth and specificity: the 'family' level, the 'component' level, and the 'technique' level. This hierarchical structure allows the level of component analysis to be tailored to the available data. For instance, more granular analyses at the 'technique' level can be conducted when large datasets are available, while broader but still informative analyses can be performed at the 'family' level when data is more limited. An online survey is currently being conducted with the authors of the trials to assess their agreement with the classification of the intervention elements. Therefore, some further adjustments and refinements to the taxonomy are anticipated in the coming months.
Parallel to this, thanks to the collaborative spirit of the trial authors Dr. Papola engaged with, he successfully gathered individual participant data (IPDs) from 30 of the 34 trials in the systematic review. This achievement represents the collection of 10,618 IPDs out of a possible 11,969, covering 89% of the total available data. This compilation forms the most extensive IPD database assembled to date in the field of global mental health. The setup for the main IPD cNMA analysis is currently being implemented.
The efficacy of task-sharing psychosocial interventions for CMDs was analyzed to identify the most effective components and their combinations through a two-step procedure conducted by Dr. Papola.
Step 1: Network Meta-Analysis (NMA)
Dr. Papola initially pooled aggregate-level trial endpoint data using a NMA technique, which allows for the simultaneous comparison of multiple interventions by utilizing both direct (head-to-head trials) and indirect (trials with a common comparator) evidence. This approach ranks treatments to identify the most effective options across a study network. PM+ intervention manual was most frequently evaluated, with 9 RCTs comparing it to enhanced treatment as usual (ETAU) and 2 RCTs against treatment as usual (TAU). Most other intervention manuals were tested only once, and none were directly compared head-to-head. This led to a network with limited connectivity. Network coherence, representing the agreement between direct and indirect evidence, was assessed within each closed loop and using the “side-splitting” method for individual comparisons. Statistical heterogeneity was evaluated using the heterogeneity variance parameter (T2 = 0.12) and the I2 statistic (I2 = 87.4%; 95% CI: 81.3%–91.5%). The Cochrane Risk of Bias tool 2 was used to assess the risk of bias, and transitivity was ensured by comparing effect modifiers across studies grouped by interventions (Figure 3).
Step 2: Component Network Meta-Analysis (cNMA)
Continuing the preparatory work with aggregate-level data, Dr. Papola then implemented a component NMA (cNMA), an innovative method that compares the constituent elements of therapies while utilizing the full network of randomized evidence. This method increased statistical power by integrating direct and indirect comparisons, preserving the randomized evidence structure, and allowing for separate effect estimates in each trial before pooling results.
A critical assumption for cNMA was the comprehensive dismantling of interventions, ensuring all components were clearly defined and aligned with their intended targets. The three-tiered treatment-specific component taxonomy developed in WP1 satisfied this requirement. A random-effects cNMA was performed using the R netmeta package, within a frequentist framework, focusing on aggregate data and incremental standardized mean differences (iSMDs) at study endpoints as the primary outcome. The cNMA approach dissected each composite intervention by modelling the individual effects of components and then adding them to estimate the total intervention effect, assuming no interaction between components and additivity of effects. The output was a network of comparisons and a ranked hierarchy of intervention components, expressed as incremental standardized mean differences (iSMDs), reflecting the added benefit of each component. The component-level network was well-connected and populated. Heterogeneity for the primary outcome was high (τ2, 0.14; τ, 0.37; I2, 90% [95% CI, 87-93]). The cNMA revealed that for the primary outcome behavioral techniques (iSMD, −0.25 [95%CI, −0.82 to 0.31]) psychoeducation (iSMD, −0.18 [95%CI, −0.57 to 0.20]) and interpersonal relationship and support (iSMD, −0.12 [95%CI, −0.64 to 0.39]) are the most beneficial components. However, results are uncertain due to the wide confidence intervals. This analysis suggests that a novel task-sharing psychosocial intervention should be based on these most efficacious components.
In the final step of the project (last year of the fellowship), the interventions in the form of treatment-specific components will be analyzed at the IPD level to determine which components have the best efficacy and for which subpopulation, and to identify the impact of participant-level prognostic factors and effect modifiers on intervention outcomes, thus achieving the ultimate goal of the project.
Step 1: Network Meta-Analysis (NMA)
Dr. Papola initially pooled aggregate-level trial endpoint data using a NMA technique, which allows for the simultaneous comparison of multiple interventions by utilizing both direct (head-to-head trials) and indirect (trials with a common comparator) evidence. This approach ranks treatments to identify the most effective options across a study network. PM+ intervention manual was most frequently evaluated, with 9 RCTs comparing it to enhanced treatment as usual (ETAU) and 2 RCTs against treatment as usual (TAU). Most other intervention manuals were tested only once, and none were directly compared head-to-head. This led to a network with limited connectivity. Network coherence, representing the agreement between direct and indirect evidence, was assessed within each closed loop and using the “side-splitting” method for individual comparisons. Statistical heterogeneity was evaluated using the heterogeneity variance parameter (T2 = 0.12) and the I2 statistic (I2 = 87.4%; 95% CI: 81.3%–91.5%). The Cochrane Risk of Bias tool 2 was used to assess the risk of bias, and transitivity was ensured by comparing effect modifiers across studies grouped by interventions (Figure 3).
Step 2: Component Network Meta-Analysis (cNMA)
Continuing the preparatory work with aggregate-level data, Dr. Papola then implemented a component NMA (cNMA), an innovative method that compares the constituent elements of therapies while utilizing the full network of randomized evidence. This method increased statistical power by integrating direct and indirect comparisons, preserving the randomized evidence structure, and allowing for separate effect estimates in each trial before pooling results.
A critical assumption for cNMA was the comprehensive dismantling of interventions, ensuring all components were clearly defined and aligned with their intended targets. The three-tiered treatment-specific component taxonomy developed in WP1 satisfied this requirement. A random-effects cNMA was performed using the R netmeta package, within a frequentist framework, focusing on aggregate data and incremental standardized mean differences (iSMDs) at study endpoints as the primary outcome. The cNMA approach dissected each composite intervention by modelling the individual effects of components and then adding them to estimate the total intervention effect, assuming no interaction between components and additivity of effects. The output was a network of comparisons and a ranked hierarchy of intervention components, expressed as incremental standardized mean differences (iSMDs), reflecting the added benefit of each component. The component-level network was well-connected and populated. Heterogeneity for the primary outcome was high (τ2, 0.14; τ, 0.37; I2, 90% [95% CI, 87-93]). The cNMA revealed that for the primary outcome behavioral techniques (iSMD, −0.25 [95%CI, −0.82 to 0.31]) psychoeducation (iSMD, −0.18 [95%CI, −0.57 to 0.20]) and interpersonal relationship and support (iSMD, −0.12 [95%CI, −0.64 to 0.39]) are the most beneficial components. However, results are uncertain due to the wide confidence intervals. This analysis suggests that a novel task-sharing psychosocial intervention should be based on these most efficacious components.
In the final step of the project (last year of the fellowship), the interventions in the form of treatment-specific components will be analyzed at the IPD level to determine which components have the best efficacy and for which subpopulation, and to identify the impact of participant-level prognostic factors and effect modifiers on intervention outcomes, thus achieving the ultimate goal of the project.