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ObjectivesAntenatal care (ANC) is an essential part of primary healthcare and its provision has expanded worldwide. There is limited evidence of large-scale cross-country studies on the impact of ANC offered to pregnant women on child health outcomes. We investigate the association of ANC in low-income and middle-income countries with short- and long-term mortality and nutritional child outcomes.SettingWe used nationally representative health and welfare data from 193 Demographic and Health Surveys conducted between 1990 and 2013 from 69 low-income and middle-income countries for women of reproductive age (15–49 years), their children and their respective household.ParticipantsThe analytical sample consisted of 752 635 observations for neonatal mortality, 574 675 observations for infant mortality, 400 426 observations for low birth weight, 501 484 observations for stunting and 512 424 observations for underweight.Main outcomes and measuresOutcome variables are neonatal and infant mortality, low birth weight, stunting and underweight.ResultsAt least one ANC visit was associated with a 1.04% points reduced probability of neonatal mortality and a 1.07% points lower probability of infant mortality. Having at least four ANC visits and having at least once seen a skilled provider reduced the probability by an additional 0.56% and 0.42% points, respectively. At least one ANC visit is associated with a 3.82% points reduced probability of giving birth to a low birth weight baby and a 4.11 and 3.26% points reduced stunting and underweight probability. Having at least four ANC visits and at least once seen a skilled provider reduced the probability by an additional 2.83%, 1.41% and 1.90% points, respectively.ConclusionsThe currently existing and accessed ANC services in low-income and middle-income countries are directly associated with improved birth outcomes and longer-term reductions of child mortality and malnourishment.

Many Markov chain Monte Carlo techniques currently available rely on discrete-time reversible Markov processes whose transition kernels are variations of the Metropolis-Hastings algorithm. We explore and generalize an alternative scheme recently introduced in the physics literature (Peters and de With 2012) where the target distribution is explored using a continuous-time nonreversible piecewise-deterministic Markov process. In the Metropolis-Hastings algorithm, a trial move to a region of lower target density, equivalently of higher \"energy,\" than the current state can be rejected with positive probability. In this alternative approach, a particle moves along straight lines around the space and, when facing a high energy barrier, it is not rejected but its path is modified by bouncing against this barrier. By reformulating this algorithm using inhomogeneous Poisson processes, we exploit standard sampling techniques to simulate exactly this Markov process in a wide range of scenarios of interest. Additionally, when the target distribution is given by a product of factors dependent only on subsets of the state variables, such as the posterior distribution associated with a probabilistic graphical model, this method can be modified to take advantage of this structure by allowing computationally cheaper \"local\" bounces, which only involve the state variables associated with a factor, while the other state variables keep on evolving. In this context, by leveraging techniques from chemical kinetics, we propose several computationally efficient implementations. Experimentally, this new class of Markov chain Monte Carlo schemes compares favorably to state-of-the-art methods on various Bayesian inference tasks, including for high-dimensional models and large datasets. Supplementary materials for this article are available online.

Stein’s method for measuring convergence to a continuous target distribution relies on an operator characterizing the target and Stein factor bounds on the solutions of an associated differential equation. While such operators and bounds are readily available for a diversity of univariate targets, few multivariate targets have been analyzed. We introduce a new class of characterizing operators based on Itô diffusions and develop explicit multivariate Stein factor bounds for any target with a fast-coupling Itô diffusion. As example applications, we develop computable and convergence-determining diffusion Stein discrepancies for log-concave, heavy-tailed and multimodal targets and use these quality measures to select the hyperparameters of biased Markov chain Monte Carlo (MCMC) samplers, compare random and deterministic quadrature rules and quantify bias-variance tradeoffs in approximate MCMC. Our results establish a near-linear relationship between diffusion Stein discrepancies and Wasserstein distances, improving upon past work even for strongly log-concave targets. The exposed relationship between Stein factors and Markov process coupling may be of independent interest.

We study the problem of sampling high and infinite dimensional target measures arising in applications such as conditioned diffusions and inverse problems. We focus on those that arise from approximating measures on Hilbert spaces defined via a density with respect to a Gaussian reference measure. We consider the Metropolis–Hastings algorithm that adds an accept–reject mechanism to a Markov chain proposal in order to make the chain reversible with respect to the target measure. We focus on cases where the proposal is either a Gaussian random walk (RWM) with covariance equal to that of the reference measure or an Ornstein–Uhlenbeck proposal (pCN) for which the reference measure is invariant. Previous results in terms of scaling and diffusion limits suggested that the pCN has a convergence rate that is independent of the dimension while the RWM method has undesirable dimension-dependent behaviour. We confirm this claim by exhibiting a dimension-independent Wasserstein spectral gap for pCN algorithm for a large class of target measures. In our setting this Wasserstein spectral gap implies an L2-spectral gap. We use both spectral gaps to show that the ergodic average satisfies a strong law of large numbers, the central limit theorem and nonasymptotic bounds on the mean square error, all dimension independent. In contrast we show that the spectral gap of the RWM algorithm applied to the reference measures degenerates as the dimension tends to infinity.

Mesenchymal stromal cells (MSCs) and their extracellular vesicles (EVs) exert profound anti-inflammatory and regenerative effects in inflammation and tissue damage, which makes them an attractive tool for cellular therapies. In this study we have assessed the inducible immunoregulatory properties of MSCs and their EVs upon stimulation with different combinations of cytokines. First, we found that MSCs primed with IFN-γ, TNF-α and IL-1β, upregulate the expression of PD-1 ligands, as crucial mediators of their immunomodulatory activity. Further, primed MSCs and MSC-EVs, compared to unstimulated MSCs and MSC-EVs, had increased immunosuppressive effects on activated T cells and mediated an enhanced induction of regulatory T cells, in a PD-1 dependent manner. Importantly, EVs derived from primed MSCs reduced the clinical score and prolonged the survival of mice in a model of graft-versus-host disease. These effects could be reversed in vitro and in vivo by adding neutralizing antibodies directed against PD-L1 and PD-L2 to both, MSCs and their EVs. In conclusion, our data reveal a priming strategy that potentiates the immunoregulatory function of MSCs and their EVs. This concept also provides new opportunities to improve the clinical applicability and efficiency of cellular or EV-based therapeutic MSC products.

Evidence on where in the hypertension care process individuals are lost to care, and how this varies among states and population groups in a country as large as India, is essential for the design of targeted interventions and to monitor progress. Yet, to our knowledge, there has not yet been a nationally representative analysis of the proportion of adults who reach each step of the hypertension care process in India. This study aimed to determine (i) the proportion of adults with hypertension who have been screened, are aware of their diagnosis, take antihypertensive treatment, and have achieved control and (ii) the variation of these care indicators among states and sociodemographic groups. We used data from a nationally representative household survey carried out from 20 January 2015 to 4 December 2016 among individuals aged 15-49 years in all states and union territories (hereafter \"states\") of the country. The stages of the care process-computed among those with hypertension at the time of the survey-were (i) having ever had one's blood pressure (BP) measured before the survey (\"screened\"), (ii) having been diagnosed (\"aware\"), (iii) currently taking BP-lowering medication (\"treated\"), and (iv) reporting being treated and not having a raised BP (\"controlled\"). We disaggregated these stages by state, rural-urban residence, sex, age group, body mass index, tobacco consumption, household wealth quintile, education, and marital status. In total, 731,864 participants were included in the analysis. Hypertension prevalence was 18.1% (95% CI 17.8%-18.4%). Among those with hypertension, 76.1% (95% CI 75.3%-76.8%) had ever received a BP measurement, 44.7% (95% CI 43.6%-45.8%) were aware of their diagnosis, 13.3% (95% CI 12.9%-13.8%) were treated, and 7.9% (95% CI 7.6%-8.3%) had achieved control. Male sex, rural location, lower household wealth, and not being married were associated with greater losses at each step of the care process. Between states, control among individuals with hypertension varied from 2.4% (95% CI 1.7%-3.3%) in Nagaland to 21.0% (95% CI 9.8%-39.6%) in Daman and Diu. At 38.0% (95% CI 36.3%-39.0%), 28.8% (95% CI 28.5%-29.2%), 28.4% (95% CI 27.7%-29.0%), and 28.4% (95% CI 27.8%-29.0%), respectively, Puducherry, Tamil Nadu, Sikkim, and Haryana had the highest proportion of all adults (irrespective of hypertension status) in the sampled age range who had hypertension but did not achieve control. The main limitation of this study is that its results cannot be generalized to adults aged 50 years and older-the population group in which hypertension is most common. Hypertension prevalence in India is high, but the proportion of adults with hypertension who are aware of their diagnosis, are treated, and achieve control is low. Even after adjusting for states' economic development, there is large variation among states in health system performance in the management of hypertension. Improvements in access to hypertension diagnosis and treatment are especially important among men, in rural areas, and in populations with lower household wealth.

AbstractThe CONSORT 2010 (Consolidated Standards of Reporting Trials) statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency when evaluating new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes.The CONSORT-AI extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI. Both guidelines were developed through a staged consensus process, involving a literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed on in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants).The CONSORT-AI extension includes 14 new items, which were considered sufficiently important for AI interventions, that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and providing analysis of error cases.CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.

A new approach for fortification of drinking water is presented for combating iron deficiency anemia (IDA) worldwide. The idea is to leach Fe from a bed containing granular metallic iron (Fe 0 ), primarily using ascorbic acid (AA). AA forms very stable and bioavailable complexes with ferrous iron (Fe II ). Calculated amounts of the Fe II -AA solution can be added daily to the drinking water of households or day-care centers for children and adults (e.g. hospitals, kindergartens/schools, refugee camps) to cover the Fe needs of the populations. Granular Fe 0 (e.g., sponge iron) in filters is regarded as a locally available Fe carrier in low-income settings, and, AA is also considered to be affordable in low-income countries. The primary idea of this concept is to stabilize Fe II from the Fe 0 filter by using an appropriate AA solution. An experiment showed that up to 12 mg Fe can be daily leached from 1.0 g of a commercial sponge iron using a 2 mM AA solution. Fe fortification of safe drinking water is a practicable, affordable and efficient method for reducing IDA in low-income communities.

As artificial intelligence moves into the realm of clinical trials, consideration is needed on whether the current CONSORT and SPIRIT reporting statements are sufficient to ensure transparency.