Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn

The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements -- effect definitions with causal interpretation, clarification of assumptions required for effect identification, and an expanding array of options for effect estimation. However, the literature on these results is fast-growing and complex, which may be confusing to researchers unfamiliar with causal inference or unfamiliar with mediation. The goal of this paper is to help ease the understanding and adoption of causal mediation analysis. It starts by highlighting a key difference between the causal inference and traditional approaches to mediation analysis and making a case for the need for explicit causal thinking and the causal inference approach in mediation analysis. It then explains in as-plain-as-possible language existing effect types, paying special attention to motivating these effects with different types of research questions, and using concrete examples for illustration. This presentation differentiates two perspectives (or purposes of analysis): the explanatory perspective (aiming to explain the total effect) and the interventional perspective (asking questions about hypothetical interventions on the exposure and mediator, or hypothetically modified exposures). For the latter perspective, the paper proposes tapping into a general class of interventional effects that contains as special cases most of the usual effect types -- interventional direct and indirect effects, controlled direct effects and also a generalized interventional direct effect type, as well as the total effect and overall effect. This general class allows flexible effect definitions which better match many research questions than the standard interventional direct and indirect effects

The Story of South African Child Welfare: A History of the Present

Though significant transformation has occurred in post-apartheid South Africa, extensive poverty, AIDS and violence present major challenges. The capacity of families and local networks, undermined by apartheid policies, continue to be depleted, leaving children vulnerable. During the apartheid era, the child welfare sector, despite its intention of supporting children and families, utilized interventions that failed to address the needs of the majority and weakened family life. Post-apartheid, government has presented Developmental Social Welfare—with its family-centered, rights-oriented, community-based, participatory, generalist and intersectoral approach—as an indigenous correction to the previous expert-driven, pathologizing, individualistic, discriminatory and costly approaches. Employing a Foucauldian genealogy or “history of the present”, this study explores the consistencies and shifts in current and historical child welfare discourses, reflected in more than 200 agency and government documents. The main finding is that the Child Protection Discourse, having been a determining discourse in the apartheid era, has remained at the forefront of child welfare thinking. The narrative, reinforced by an International Anglo-American Discourse, eclipses the Developmental Discourse, with the child welfare community continuing to employ a harm/safety-based orientation rather than a holistic understanding to construct child welfare. This study provides new insights about the manner in which the Developmental Discourse is weakened. Child Protection oriented policies intersect well with the governmental shift towards individualized, neo-liberal philosophies and with a Rights Discourse. In addition, the resource and personnel crises emphasize remedial rather than preventive interventions as core activities. The inadequate and inconsistent conceptualization of a Developmental Discourse has allowed the language of Transformation (participation, equity, indigenization, empowerment and prevention) to be reconstructed within a Child Protection Discourse. Governmentality operates through the Developmental and Child Protection discourses, racialized subjectivities intersecting with notions of the ‘poor beneficiary’ to entrench intrusive and paternalistic measures. As the Child Protection discourse is central, subjects tend to be scrutinized, individualized and blamed for their problems by social workers constructed as experts rather than as facilitators and enablers. The thesis concludes by considering ways in which the South African child welfare community can resist the influence of the Child Protection Discourse and reinforce a Developmental child welfare discourse

BINATIONAL FARMING FAMILIES OF SOUTHERN APPALACHIA AND THE MEXICAN BAJIO

Over the last four decades, farming families throughout North America experienced significant transitions due, in part, to the implementation of the North American Free Trade Agreement. This multi-sited dissertation investigates the ways in which a network of binational (Mexican-American) families organize their small- to mid-scale farming enterprises, engage in global networks as food producers, and contribute to rural economies in the southeastern U.S. and the Mexican Bajío. To mitigate difficult transitions that came with the globalizing of agri-food markets, members of this extended family group created collaborative, kin-based arrangements to produce, distribute, and market fresh-market fruits and vegetables in the foothills of southern Appalachia and basic grains in the foothills of the Mexican Bajío. Members of extended binational families regularly negotiate social, economic, and political borders within and across regions, genders, and generations. This study shows how these binational kin use cooperative practices to navigate two distinct, yet interrelated, contemporary agricultural political economic environments in North America. The study counter-constructs stereotypes of Latinx and their roles in southeastern U.S. agriculture by focusing on a vertically integrated, kin group of allied, migrant farming families and theorizing them as binational collective strategists. Their stories and strategies provide insight into the importance of temporalities and practices of kin relatedness to agri-food enterprises and suggest possibilities for alternative distributions of surplus value within the globalized agri-food system

Understanding the Roots of Radicalisation on Twitter

In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies. Researchers and governments are investing in the creation of advanced information technologies to identify and counter extremism through intelligent large-scale analysis of online data. However, to the best of our knowledge, these technologies are neither based on, nor do they take advantage of, the existing theories and studies of radicalisation. In this paper we propose a computational approach for detecting and predicting the radicalisation influence a user is exposed to, grounded on the notion of ’roots of radicalisation’ from social science models. This approach has been applied to analyse and compare the radicalisation level of 112 pro-ISIS vs.112 “general" Twitter users. Our results show the effectiveness of our proposed algorithms in detecting and predicting radicalisation influence, obtaining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. While this is an initial attempt towards the effective combination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation

Unambiguous detection of cardiac Pi using long TM 31P STEAM

Synopsis Inorganic Phosphate is a resonance that holds important information on the metabolic state of tissues. From its resonance frequency, intracellular pH can be derived. The ratio of P to PCr or ATP are also important markers. Unlike in other tissues, myocardial P is frequently hidden underneath blood DPG signals. Using STEAM's T delay to be one cardiac cycle, blood-pool originating signals are gone and the Pi resonance is clearly visible. In 3 subjects, P signal was detected and quantified. The signal was around 4.89±0.02ppm, corresponding to a pH of 7.08±0.02. This is a breakthrough for the investigation of cardiac metabolism.This work was supported by the Austrian Science Fund (FWF) project J 4043 as well as by the Sir Henry Dale Fellowship from the Wellcome Trust and the Royal Society (098436/Z/12/Z)

Progress in Reading Literacy Study: Australia’s results from PIRLS 2021

Progress in International Reading Literacy Study (PIRLS) is a large-scale assessment that measures how effective countries are in teaching reading literacy. Conducted every five years since 2001 (with Australia participating since 2011), PIRLS provides information about how to improve teaching and learning so that young students become accomplished and self-sufficient readers. In Australia, almost 5,500 Year 4 students participated in PIRLS 2021. These students completed tests in reading comprehension and answered questionnaires on their background and experiences in learning reading at school. To inform educational policy in the participating countries, alongside the assessment of reading literacy, PIRLS also routinely collects extensive background information that addresses concerns about the quantity, quality and content of instruction. This background information is collected through a series of questionnaires for students, teachers, principals and curriculum specialists

Immunotherapy for early triple negative breast cancer: research agenda for the next decade

Breast cancer; Drug development; ImmunoeditingCàncer de mama; Desenvolupament de medicaments; ImmunoedicióCáncer de mama; Desarrollo de fármacos; InmunoediciónFor decades, the systemic treatment of localized triple negative breast cancer (TNBC) has exclusively relied on chemotherapy. Recent advancements, however, are rapidly reshaping the treatment algorithms for this disease. The addition of pembrolizumab to neoadjuvant chemotherapy has indeed shown to significantly improve event-free survival for stage II–III TNBC, leading to its establishment as new standard of care in this setting. This landmark advancement has however raised several important scientific questions. Indeed, we desperately need strategies to identify upfront patients deriving benefit from the addition of immunotherapy. Moreover, the best integration of pembrolizumab with further recent advancements (capecitabine, olaparib) is yet to be defined. Lastly, extensive efforts are needed to minimize the impact on patients of immune-related adverse events and financial toxicity. The next decade of clinical research will be key to overcome these challenges, and ultimately learn how to optimally integrate immunotherapy in the treatment landscape of TNBC.Supported by an American-Italian Cancer Foundation Post-Doctoral Research Fellowship

Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation details

Background: Randomized controlled trials are often used to inform policy and practice for broad populations. The average treatment effect (ATE) for a target population, however, may be different from the ATE observed in a trial if there are effect modifiers whose distribution in the target population is different that from that in the trial. Methods exist to use trial data to estimate the target population ATE, provided the distributions of treatment effect modifiers are observed in both the trial and target population -- an assumption that may not hold in practice. Methods: The proposed sensitivity analyses address the situation where a treatment effect modifier is observed in the trial but not the target population. These methods are based on an outcome model or the combination of such a model and weighting adjustment for observed differences between the trial sample and target population. They accommodate several types of outcome models: linear models (including single time outcome and pre- and post-treatment outcomes) for additive effects, and models with log or logit link for multiplicative effects. We clarify the methods' assumptions and provide detailed implementation instructions. Illustration: We illustrate the methods using an example generalizing the effects of an HIV treatment regimen from a randomized trial to a relevant target population. Conclusion: These methods allow researchers and decision-makers to have more appropriate confidence when drawing conclusions about target population effects

Shared Control Individuals in Health Policy Evaluations with Application to Medical Cannabis Laws

Health policy researchers often have questions about the effects of a policy implemented at some cluster-level unit, e.g., states, counties, hospitals, etc. on individual-level outcomes collected over multiple time periods. Stacked difference-in-differences is an increasingly popular way to estimate these effects. This approach involves estimating treatment effects for each policy-implementing unit, then, if scientifically appropriate, aggregating them to an average effect estimate. However, when individual-level data are available and non-implementing units are used as comparators for multiple policy-implementing units, data from untreated individuals may be used across multiple analyses, thereby inducing correlation between effect estimates. Existing methods do not quantify or account for this sharing of controls. Here, we describe a stacked difference-in-differences study investigating the effects of state medical cannabis laws on treatment for chronic pain management that motivated this work, discuss a framework for estimating and managing this correlation due to shared control individuals, and show how accounting for it affects the substantive results