Evolutionary dynamics within and among competing groups.

Biological and social systems are structured at multiple scales, and the incentives of individuals who interact in a group may diverge from the collective incentive of the group as a whole. Mechanisms to resolve this tension are responsible for profound transitions in evolutionary history, including the origin of cellular life, multicellular life, and even societies. Here, we synthesize a growing literature that extends evolutionary game theory to describe multilevel evolutionary dynamics, using nested birth-death processes and partial differential equations to model natural selection acting on competition within and among groups of individuals. We analyze how mechanisms known to promote cooperation within a single group-including assortment, reciprocity, and population structure-alter evolutionary outcomes in the presence of competition among groups. We find that population structures most conducive to cooperation in multiscale systems can differ from those most conducive within a single group. Likewise, for competitive interactions with a continuous range of strategies we find that among-group selection may fail to produce socially optimal outcomes, but it can nonetheless produce second-best solutions that balance individual incentives to defect with the collective incentives for cooperation. We conclude by describing the broad applicability of multiscale evolutionary models to problems ranging from the production of diffusible metabolites in microbes to the management of common-pool resources in human societies.

Unfolding sustainability transitions in food systems: Insights from UK and French trajectories.

While the negative environmental, social and health impacts of the current food system have been acknowledged and evidenced for several decades, the recent and current transformations in food systems at diverse scales are not yet addressing the many inter-related stakes at play. Due to the much wider set of interactions in this consumption-production system, new conceptual tools are required for understanding and assessing sustainability transitions and what prevents them. The article will draw on the cases of France and the UK to examine these countries' national food systems' historical trajectories and suggest a periodization of these in order to reveal common characteristics and differences. This will show that despite common major trends and common transition or inertia mechanisms, pathways differ, especially from the 1990s, due to different configurations of power relationships between the state, economic actors and civil society in a context of an increasing competition between sustainability narratives that leads to an increasing fragmentation in food systems. It will lead us to join the recent progress in the sustainability transitions' community towards a shift in the analysis from a focus on niches' trajectories and effects to a deeper focus on power configurations and competing narratives, as well as to suggest a larger inclusion of socio-ecological and spatial dimensions.

Multilevel cultural evolution: From new theory to practical applications.

Evolutionary science has led to many practical applications of genetic evolution but few practical uses of cultural evolution. This is because the entire study of evolution was gene centric for most of the 20th century, relegating the study and application of human cultural change to other disciplines. The formal study of human cultural evolution began in the 1970s and has matured to the point of deriving practical applications. We provide an overview of these developments and examples for the topic areas of complex systems science and engineering, economics and business, mental health and well-being, and global change efforts.

How policies and actor strategies affect electric vehicle diffusion and wider sustainability transitions.

Implementing electromobility is a central component in the de-carbonization of personal mobility. In recent years, the absolute number of electric vehicles (EVs) and their market share has increased sharply in many countries. This paper focuses on Norway, a pioneer market for EVs that other countries can learn from. The analysis highlights how a combination of local and national policies over a 30-y period, which targeted both industry development and vehicle demand, were important drivers of this development. It also highlights the importance of advocacy groups and strong networks in promoting EVs, as well as changes in user preferences. The paper demonstrates how the EV diffusion has been driven by alignments of multiple processes across different levels, involving interactions between multiple actors and social groups with different interests and views about desirable futures as described by the multi-level perspective (MLP). Building on the MLP, the study of EV diffusion in Norway illustrates how niches are often sustained through demonstrations, experimentation, strategic alliances, and actors securing favorable political and economic conditions. Further, it shows how local or national niches may depend on international regime actors, such as the car manufacturing industry and policies developed abroad. The paper also explores how the introduction of EVs has opened for wider effects, including innovation within production-consumption systems beyond mobility. Based on this analysis, we argue for a nuanced perspective on the relationship between incremental, regime-internal innovation, and wider transformative changes, where the merits of societal learning and experience with battery electricity for transportation are highlighted.

Sustainability transitions in consumption-production systems.

The need for faster and deeper transitions toward more sustainable development pathways is now widely recognized. How to meet that need has been at the center of a growing body of academic research and real-world policy implementation. This paper presents our perspective on some of the most powerful insights that have emerged from this ongoing work. In particular, we highlight insights on how sustainability transitions can be usefully conceptualized, how they come about and evolve, and how they can be shaped and guided through deliberate policy interventions. Throughout the paper, we also highlight some of the many how questions that remain unresolved and on which progress would be especially helpful for the pursuit of sustainable development. Our approach to these "how" questions on sustainability transitions draws on two strands of solution-driven research and policy advice: one emerging from studies of how human societies interact with nature and the other emerging from studies of how those societies interact with their technologies. Consumption-production systems have been a focus of extensive work in both strands. To help build bridges between them, we recently brought together a cross-section of relevant scholars for a PNAS Special Feature on "Sustainability transitions in consumption-production systems." Their contributions are summarized in a companion paper we have written to introduce the Special Feature [F. W. Geels, F. Kern, W. C. Clark, Proc. Natl. Acad. Sci. U.S.A. (2023)]. We draw on that work in the Perspective we present here as well as our reading of the relevant literatures.

Identifying somatic fingerprints of cancers defined by germline and environmental risk factors.

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.

Integration of C3H15-mediated transcriptional and post-transcriptional regulation confers plant thermotolerance in Arabidopsis.

Heat stress is an environmental factor that significantly threatens crop production worldwide. Nevertheless, the molecular mechanisms governing plant responses to heat stress are not fully understood. Plant zinc finger CCCH proteins have roles in stress responses as well as growth and development through protein-RNA, protein-DNA, and protein-protein interactions. Here, we reveal an integrated multi-level regulation of plant thermotolerance that is mediated by the CCCH protein C3H15 in Arabidopsis. Heat stress rapidly suppressed C3H15 transcription, which attenuated C3H15-inhibited expression of its target gene HEAT SHOCK TRANSCRIPTION FACTOR A2 (HSFA2), a central regulator of heat stress response (HSR), thereby activating HEAT SHOCK COGNATE 70 (HSC70.3) expression. The RING-type E3 ligase MED25-BINDING RING-H2 PROTEIN 2 (MBR2) was identified as an interacting partner of C3H15. The mbr2 mutant was susceptible to heat stress compared to wild-type plants, whereas plants overexpressing MBR2 showed increased heat tolerance. MBR2-dependent ubiquitination mediated the degradation of phosphorylated C3H15 protein in the cytoplasm, which was enhanced by heat stress. Consistently, heat sensitivities of C3H15 overexpression lines increased in MBR2 loss-of-function and decreased in MBR2 overexpression backgrounds. Heat stress-induced accumulation of HSC70.3 promoted MBR2-mediated degradation of C3H15 protein, implying that an auto-regulatory loop involving C3H15, HSFA2, and HSC70.3 regulates HSR. Heat stress also led to the accumulation of C3H15 in stress granules (SGs), a kind of cytoplasmic RNA granule. This study advances our understanding of the mechanisms plants use to respond to heat stress, which will facilitate technologies to improve thermotolerance in crops.

MulinforCPI: enhancing precision of compound-protein interaction prediction through novel perspectives on multi-level information integration.

Forecasting the interaction between compounds and proteins is crucial for discovering new drugs. However, previous sequence-based studies have not utilized three-dimensional (3D) information on compounds and proteins, such as atom coordinates and distance matrices, to predict binding affinity. Furthermore, numerous widely adopted computational techniques have relied on sequences of amino acid characters for protein representations. This approach may constrain the model's ability to capture meaningful biochemical features, impeding a more comprehensive understanding of the underlying proteins. Here, we propose a two-step deep learning strategy named MulinforCPI that incorporates transfer learning techniques with multi-level resolution features to overcome these limitations. Our approach leverages 3D information from both proteins and compounds and acquires a profound understanding of the atomic-level features of proteins. Besides, our research highlights the divide between first-principle and data-driven methods, offering new research prospects for compound-protein interaction tasks. We applied the proposed method to six datasets: Davis, Metz, KIBA, CASF-2016, DUD-E and BindingDB, to evaluate the effectiveness of our approach.

Community-level evolutionary processes: Linking community genetics with replicator-interactor theory.

Understanding community-level selection using Lewontin's criteria requires both community-level inheritance and community-level heritability, and in the discipline of community and ecosystem genetics, these are often conflated. While there are existing studies that show the possibility of both, these studies impose community-level inheritance as a product of the experimental design. For this reason, these experiments provide only weak support for the existence of community-level selection in nature. By contrast, treating communities as interactors (in line with Hull's replicator-interactor framework or Dawkins's idea of the "extended phenotype") provides a more plausible and empirically supportable model for the role of ecological communities in the evolutionary process.

Metal-organic cycle-based multistage assemblies.

It is well known that chemical compositions and structural arrangements of materials have a great influence on their resultant properties. Diverse functional materials have been constructed by using either biomolecules (peptides, DNA, and RNA) in nature or artificially synthesized molecules (polymers and pillararenes). The relationships between traditional building blocks (such as peptides) have been widely investigated, for example how hydrogen bonds work in the peptide multistage assembly process. However, in contrast to traditional covalent bond-based building blocks-based assembly, suprastructures formed by noncovalent bonds are more influenced by specific bond features, but to date only a few results have been reported based on noncovalent bond-based building block multistage assembly. Here, three metal­organic cycles (MOCs) were used to show how coordination bonds influence the bimetallacycle conformation then lead to the topology differences of MOC multilevel ordered materials. It was found that the coordination linker (isophthalate-Pt-pyridine) is an important factor to tune the shape and size of the MOC-derived suprastructures.

Programmable Ferroelectricity in Hf0.5Zr0.5O2 Enabled by Oxygen Defect Engineering.

Ferroelectricity, especially the Si-compatible type recently observed in hafnia-based materials, is technologically useful for modern memory and logic applications, but it is challenging to differentiate intrinsic ferroelectric polarization from the polar phase and oxygen vacancy. Here, we report electrically controllable ferroelectricity in a Hf0.5Zr0.5O2-based heterostructure with Sr-doped LaMnO3, a mixed ionic-electronic conductor, as an electrode. Electrically reversible extraction and insertion of an oxygen vacancy into Hf0.5Zr0.5O2 are macroscopically characterized and atomically imaged in situ. Utilizing this reversible process, we achieved multilevel polarization states modulated by the electric field. Our study demonstrates the usefulness of the mixed conductor to repair, create, manipulate, and utilize advanced ferroelectric functionality. Furthermore, the programmed ferroelectric heterostructures with Si-compatible doped hafnia are desirable for the development of future ferroelectric electronics.

Co-evolution of gene transfer agents and their alphaproteobacterial hosts.

Gene transfer agents (GTAs) are enigmatic elements that resemble small viruses and are known to be produced during nutritional stress by some bacteria and archaea. The production of GTAs is regulated by quorum sensing, under which a small fraction of the population acts as GTA producers, while the rest becomes GTA recipients. In contrast to canonical viruses, GTAs cannot propagate themselves because they package pieces of the producing cell's genome. In alphaproteobacteria, GTAs are mostly vertically inherited and reside in their hosts' genomes for hundreds of millions of years. While GTAs' ability to transfer genetic material within a population and their long-term preservation suggest an increased fitness of GTA-producing microbes, the associated benefits and type of selection that maintains GTAs are poorly understood. By comparing rates of evolutionary change in GTA genes to the rates in gene families abundantly present across 293 alphaproteobacterial genomes, we detected 59 gene families that likely co-evolve with GTA genes. These gene families are predominantly involved in stress response, DNA repair, and biofilm formation. We hypothesize that biofilm formation enables the physical proximity of GTA-producing cells, limiting GTA-derived benefits only to a group of closely related cells. We further conjecture that the population structure of biofilm-forming sub-populations ensures that the trait of GTA production is maintained despite the inevitable rise of "cheating" genotypes. Because release of GTA particles kills the producing cell, maintenance of GTAs is an exciting example of social evolution in a microbial population.IMPORTANCEGene transfer agents (GTAs) are viruses domesticated by some archaea and bacteria as vehicles for carrying pieces of the host genome. Produced under certain environmental conditions, GTA particles can deliver DNA to neighboring, closely related cells. The function of GTAs remains uncertain. While making GTAs is suicidal for a cell, GTA-encoding genes are widespread in genomes of alphaproteobacteria. Such GTA persistence implies functional benefits but raises questions about how selection maintains this lethal trait. By showing that GTA genes co-evolve with genes involved in stress response, DNA repair, and biofilm formation, we provide support for the hypothesis that GTAs facilitate DNA exchange during the stress conditions and present a model for how GTAs persist in biofilm-forming bacterial populations despite being lethal.

Depression at the intersection of race/ethnicity, sex/gender, and sexual orientation in a nationally representative sample of US adults: A design-weighted intersectional MAIHDA.

This study examined how race/ethnicity, sex/gender, and sexual orientation intersect under interlocking systems of oppression to socially pattern depression among US adults. With cross-sectional data from the 2015-2020 National Survey on Drug Use and Health (NSDUH; n=234,722), we conducted design-weighted multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) under an intersectional framework to predict past-year and lifetime major depressive episode (MDE). With 42 intersectional groups constructed from seven race/ethnicity, two sex/gender, and three sexual orientation categories, we estimated age-standardized prevalence and excess/reduced prevalence attributable to two-way or higher interaction effects. Models revealed heterogeneity across groups, with prevalence ranging from 1.9-19.7% (past-year) and 4.5-36.5% (lifetime). Approximately 12.7% (past-year) and 12.5% (lifetime) of total individual variance were attributable to between-group differences, indicating key relevance of intersectional groups in describing the population distribution of depression. Main effects indicated, on average, people who were White, women, gay/lesbian, or bisexual had greater odds of MDE. Main effects explained most between-group variance. Interaction effects (past-year: 10.1%; lifetime: 16.5%) indicated a further source of heterogeneity around averages with groups experiencing excess/reduced prevalence compared to main effects expectations. We extend the MAIHDA framework to calculate nationally representative estimates from complex sample survey data using design-weighted, Bayesian methods.

Social isolation, depression, and anxiety among young adult cancer survivors: The mediating role of social connectedness.

BACKGROUND: Social isolation and social connectedness are health determinants and aspects of social well-being with strong associations with psychological distress. This study evaluated relationships among social isolation, social connectedness, and psychological distress (i.e., depression, anxiety) over 1 year in young adult (YA) cancer survivors 18-39 years old. METHODS: Participants were YAs in a large cohort study that completed questionnaires every 2 months for 1 year. Social isolation, aspects of social connectedness (i.e., companionship, emotional support, instrumental support, and informational support), depression, and anxiety were assessed with Patient-Reported Outcomes Measurement Information System short form measures. Mixed-effect models were used to evaluate changes over time. Confirmatory factor analysis and multilevel structural equation modeling were used to define social connectedness as a latent construct and determine whether relationships between social isolation and psychological distress were mediated by social connectedness. RESULTS: Participants (N = 304) were mean (M) = 33.5 years old (SD = 4.7) and M = 4.5 years (SD = 3.5) post-initial cancer diagnosis. Most participants were female (67.4%) and non-Hispanic White (68.4%). Average scores for social well-being and psychological distress were within normative ranges and did not change (p values >.05). However, large proportions of participants reported at least mild social isolation (27%-30%), depressive symptoms (36%-37%), and symptoms of anxiety (49%-51%) at each time point. Across participants, more social isolation was related to less social connectedness (p values <.001), more depressive symptoms (p < .001), and more symptoms of anxiety (p < .001). Social connectedness mediated the relationship between social isolation and depression (p = .004), but not anxiety (p > .05). CONCLUSIONS: Social isolation and connectedness could be intervention targets for reducing depression among YA cancer survivors.

Racial disparities across multiple stages of the deceased organ donation process.

Pervasive structural violence causes higher organ failure rates among Black Americans and excess Black potential deceased organ donors. Underuse of Black donors would exacerbate organ shortages that disproportionately harm Black transplant candidates. This study investigates racial differences in transit between distinct donation steps among 132 968 potential donors across 557 hospitals and 6 Organ Procurement Organizations (OPOs) from 2015 through 2021. Multilevel multistate modeling with patient covariates and OPO random effects shows adjusted likelihoods (95% confidence interval [CI]) of non-Black versus Black patients transitioning from OPO referral to approach: odds ratio (OR) 1.23 (95% CI 1.18, 1.27), approach to authorization: OR 1.64 (95% CI 1.56, 1.72), authorization to procurement: OR 1.08 (95% CI 1.02, 1.14), and procurement to transplant: OR 0.99 (95% CI 0.93, 1.04). Overall organ utilization rates for Black, Latino, White, and other OPO referrals were 5.88%, 8.17%, 6.78%, and 5.24%, respectively. Adjusting for patient covariates and hospital and OPO random effects, multilevel logistic models estimated that compared with Black patients, Latino, White, and other patients had ORs of organ utilization of 1.82 (95% CI 1.61, 2.04), 3.19 (95% CI 2.91, 3.50), and 1.25 (95% CI 1.06, 1.47), respectively. Nationwide in 2022, donor conversion disparities likely lost more than 1800 donors-70% of whom would have been Black. Achieving racial equity for transplant candidates will require reducing racial disparities in organ donation.

Associations of prior concussion severity with brain microstructure using mean apparent propagator magnetic resonance imaging.

Magnetic resonance imaging (MRI) diffusion studies have shown chronic microstructural tissue abnormalities in athletes with history of concussion, but with inconsistent findings. Concussions with post-traumatic amnesia (PTA) and/or loss of consciousness (LOC) have been connected to greater physiological injury. The novel mean apparent propagator (MAP) MRI is expected to be more sensitive to such tissue injury than the conventional diffusion tensor imaging. This study examined effects of prior concussion severity on microstructure with MAP-MRI. Collegiate-aged athletes (N = 111, 38 females; ≥6 months since most recent concussion, if present) completed semistructured interviews to determine the presence of prior concussion and associated injury characteristics, including PTA and LOC. MAP-MRI metrics (mean non-Gaussian diffusion [NG Mean], return-to-origin probability [RTOP], and mean square displacement [MSD]) were calculated from multi-shell diffusion data, then evaluated for associations with concussion severity through group comparisons in a primary model (athletes with/without prior concussion) and two secondary models (athletes with/without prior concussion with PTA and/or LOC, and athletes with/without prior concussion with LOC only). Bayesian multilevel modeling estimated models in regions of interest (ROI) in white matter and subcortical gray matter, separately. In gray matter, the primary model showed decreased NG Mean and RTOP in the bilateral pallidum and decreased NG Mean in the left putamen with prior concussion. In white matter, lower NG Mean with prior concussion was present in all ROI across all models and was further decreased with LOC. However, only prior concussion with LOC was associated with decreased RTOP and increased MSD across ROI. Exploratory analyses conducted separately in male and female athletes indicate associations in the primary model may differ by sex. Results suggest microstructural measures in gray matter are associated with a general history of concussion, while a severity-dependent association of prior concussion may exist in white matter.

Flexible evaluation of surrogacy in platform studies.

Trial-level surrogates are useful tools for improving the speed and cost effectiveness of trials but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial-level surrogate evaluation, but none, to our knowledge, for the specific setting of platform studies. As platform studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. However, they also offer a set of statistical issues including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner so that treatment effects on the surrogate can be used to reliably predict treatment effects on the clinical outcome. In simulations, we find that our proposed method is superior to a simple, but fairly standard, hierarchical Bayesian method. We demonstrate how our method can be used in a simulated illustrative example (based on the ProBio trial), in which we are able to identify clusters where the surrogate is, and is not useful. We plan to apply our method to the ProBio trial, once it is completed.

Interpretable principal component analysis for multilevel multivariate functional data.

Many studies collect functional data from multiple subjects that have both multilevel and multivariate structures. An example of such data comes from popular neuroscience experiments where participants' brain activity is recorded using modalities such as electroencephalography and summarized as power within multiple time-varying frequency bands within multiple electrodes, or brain regions. Summarizing the joint variation across multiple frequency bands for both whole-brain variability between subjects, as well as location-variation within subjects, can help to explain neural reactions to stimuli. This article introduces a novel approach to conducting interpretable principal components analysis on multilevel multivariate functional data that decomposes total variation into subject-level and replicate-within-subject-level (i.e., electrode-level) variation and provides interpretable components that can be both sparse among variates (e.g., frequency bands) and have localized support over time within each frequency band. Smoothness is achieved through a roughness penalty, while sparsity and localization of components are achieved by solving an innovative rank-one based convex optimization problem with block Frobenius and matrix $L_1$-norm-based penalties. The method is used to analyze data from a study to better understand reactions to emotional information in individuals with histories of trauma and the symptom of dissociation, revealing new neurophysiological insights into how subject- and electrode-level brain activity are associated with these phenomena. Supplementary materials for this article are available online.

Two-Stage TMLE to reduce bias and improve efficiency in cluster randomized trials.

Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on individuals in those groups. While offering many advantages, this experimental design introduces challenges that are only partially addressed by existing analytic approaches. First, outcomes are often missing for some individuals within clusters. Failing to appropriately adjust for differential outcome measurement can result in biased estimates and inference. Second, CRTs often randomize limited numbers of clusters, resulting in chance imbalances on baseline outcome predictors between arms. Failing to adaptively adjust for these imbalances and other predictive covariates can result in efficiency losses. To address these methodological gaps, we propose and evaluate a novel two-stage targeted minimum loss-based estimator to adjust for baseline covariates in a manner that optimizes precision, after controlling for baseline and postbaseline causes of missing outcomes. Finite sample simulations illustrate that our approach can nearly eliminate bias due to differential outcome measurement, while existing CRT estimators yield misleading results and inferences. Application to real data from the SEARCH community randomized trial demonstrates the gains in efficiency afforded through adaptive adjustment for baseline covariates, after controlling for missingness on individual-level outcomes.

Blurring cluster randomized trials and observational studies: Two-Stage TMLE for subsampling, missingness, and few independent units.

Cluster randomized trials (CRTs) often enroll large numbers of participants; yet due to resource constraints, only a subset of participants may be selected for outcome assessment, and those sampled may not be representative of all cluster members. Missing data also present a challenge: if sampled individuals with measured outcomes are dissimilar from those with missing outcomes, unadjusted estimates of arm-specific endpoints and the intervention effect may be biased. Further, CRTs often enroll and randomize few clusters, limiting statistical power and raising concerns about finite sample performance. Motivated by SEARCH-TB, a CRT aimed at reducing incident tuberculosis infection, we demonstrate interlocking methods to handle these challenges. First, we extend Two-Stage targeted minimum loss-based estimation to account for three sources of missingness: (i) subsampling; (ii) measurement of baseline status among those sampled; and (iii) measurement of final status among those in the incidence cohort (persons known to be at risk at baseline). Second, we critically evaluate the assumptions under which subunits of the cluster can be considered the conditionally independent unit, improving precision and statistical power but also causing the CRT to behave like an observational study. Our application to SEARCH-TB highlights the real-world impact of different assumptions on measurement and dependence; estimates relying on unrealistic assumptions suggested the intervention increased the incidence of TB infection by 18% (risk ratio [RR]=1.18, 95% confidence interval [CI]: 0.85-1.63), while estimates accounting for the sampling scheme, missingness, and within community dependence found the intervention decreased the incident TB by 27% (RR=0.73, 95% CI: 0.57-0.92).