In-utero co-exposure to toxic metals and micronutrients on childhood risk of overweight or obesity: new insight on micronutrients counteracting toxic metals.

BACKGROUND/OBJECTIVES: Low-level, in-utero exposure to toxic metals such as lead (Pb) and mercury (Hg) is widespread in the US and worldwide; and, individually, was found to be obesogenic in children. To address the literature gaps on the health effects of co-exposure to low-level toxic metals and the lack of intervention strategy, we aimed to investigate the association between in-utero co-exposure to Hg, Pb, cadmium (Cd) and childhood overweight or obesity (OWO) and whether adequate maternal micronutrients (selenium (Se) and folate) can be protective. SUBJECTS/METHODS: This study included 1442 mother-child pairs from the Boston Birth Cohort, a predominantly urban, low-income, Black, and Hispanic population, who were enrolled at birth and followed prospectively up to age 15 years. Bayesian kernel machine regression (BKMR) was applied to estimate individual and joint effects of exposures to metals and micronutrients on childhood OWO while adjusting for pertinent covariables. Stratified analyses by maternal OWO and micronutrient status were performed to identify sensitive subgroups. RESULTS: In this sample of understudied US children, low-level in-utero co-exposure to Hg, Pb, and Cd was widespread. Besides individual positive associations of maternal Hg and Pb exposure with offspring OWO, BKMR clearly indicated a positive dose-response association between in-utero co-exposure to the three toxic metals and childhood OWO. Notably, the metal mixture-OWO association was more pronounced in children born to mothers with OWO; and in such a setting, the association was greatly attenuated if mothers had higher Se and folate levels. CONCLUSIONS: In this prospective cohort of US children at high-risk of toxic metal exposure and OWO, we demonstrated that among children born to mothers with OWO, low-level in-utero co-exposure to Hg, Pb, and Cd increased the risk of childhood OWO; and that adequate maternal Se and folate levels mitigated the risk of childhood OWO. CLINICAL TRIAL REGISTRY NUMBER AND WEBSITE WHERE IT WAS OBTAINED: NCT03228875.

Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science.

Policy implementation is a key component of scaling effective chronic disease prevention and management interventions. Policy can support scale-up by mandating or incentivizing intervention adoption, but enacting a policy is only the first step. Fully implementing a policy designed to facilitate implementation of health interventions often requires a range of accompanying implementation structures, like health IT systems, and implementation strategies, like training. Decision makers need to know what policies can support intervention adoption and how to implement those policies, but to date research on policy implementation is limited and innovative methodological approaches are needed. In December 2021, the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness and the Johns Hopkins Center for Mental Health and Addiction Policy convened a forum of research experts to discuss approaches for studying policy implementation. In this report, we summarize the ideas that came out of the forum. First, we describe a motivating example focused on an Affordable Care Act Medicaid health home waiver policy used by some US states to support scale-up of an evidence-based integrated care model shown in clinical trials to improve cardiovascular care for people with serious mental illness. Second, we define key policy implementation components including structures, strategies, and outcomes. Third, we provide an overview of descriptive, predictive and associational, and causal approaches that can be used to study policy implementation. We conclude with discussion of priorities for methodological innovations in policy implementation research, with three key areas identified by forum experts: effect modification methods for making causal inferences about how policies' effects on outcomes vary based on implementation structures/strategies; causal mediation approaches for studying policy implementation mechanisms; and characterizing uncertainty in systems science models. We conclude with discussion of overarching methods considerations for studying policy implementation, including measurement of policy implementation, strategies for studying the role of context in policy implementation, and the importance of considering when establishing causality is the goal of policy implementation research.

Gender-responsive monitoring and evaluation for health systems.

Gender-responsive monitoring and evaluation (M&E) for health and health systems interventions and programs is vital to improve health, health systems, and gender equality outcomes. It can be used to identify and address gender disparities in program participation, outcomes, and benefits, as well as ensure that programs are designed and implemented in a way that is inclusive and accessible for all. While gender-responsive M&E is most effective when interventions and programs intentionally integrate a gender lens, it is relevant for all health systems programs and interventions. Within the literature, gender-responsive M&E is defined in different and diverse ways, making it difficult to operationalize. This is compounded by the complexity and multi-faceted nature of gender. Within this methodological musing, we present our evolving approach to gender-responsive M&E which we are operationalizing within the Monitoring for Gender and Equity (MAGE) project. We define gender-responsive M&E as intentionally integrating the needs, rights, preferences, and power relations among women and girls, men and boys, and gender minority individuals, as well as across social, political, economic, and health systems, in M&E processes. This is done through the integration of different types of gender data and indicators, including: sex or gender specific, sex or gender disaggregated, sex or gender specific/disaggregated which incorporate needs, rights and preferences, and gender power relations and systems indicators. Examples of each of these are included within the paper. Active approaches can also enhance the gender-responsiveness of any M&E activities, including incorporating an intersectional lens and tailoring the types of data and indicators included and processes used to the specific context. Incorporating gender into the programmatic cycle, including M&E, can lead to more fit-for-purpose, effective, and equitable programs and interventions. The framework presented in this paper provides an outline of how to do this, enabling the uptake of gender-responsive M&E.

Impact of industry 4.0 on healthcare systems of low- and middle- income countries: a systematic review.

Purpose: A growing body of empirical research has emerged, focused on leveraging Industry 4.0 technologies to develop and optimise systems within various operational contexts, including healthcare delivery. However, even though a significant number of studies have been published on application of digital technologies in enhancing delivery and health outcomes of health systems, systematic studies that review how extensively these technologies have been applied within a low- and middle-income economies' context remain scarce in the literature. This work attempts to close that gap by investigating the impact of industry 4.0 on healthcare systems in emerging economies. Methods: The study follows a systematic review approach and uses PRISMA guidelines to conduct the research and synthesise its findings. A final sample of 72 articles is selected for in-depth review following a systematic screening from an initial list of 597 results. Results: The study successfully synthesises the latest research in the subject area and reveals that, hitherto, approaches to use of digital tools have been fragmented and thus unable to provide holistic optimisation solutions for healthcare systems in low-resource settings. The analysis exposes a heavy skew towards adoption of mobile health and telemedicine technologies, with conspicuous research gaps in the use of augmented reality, additive manufacturing as well as simulation and digital twin technologies. Conclusions: The study provides researchers, health-care practitioners and systems engineers with knowledge on the state-of-the-art in healthcare systems optimisation and points out research gaps that may be addressed through future empirical studies.

Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort.

Background: Overweight or obesity (OWO) in school-age childhood tends to persist into adulthood. This study aims to address a critical need for early identification of children at high risk of developing OWO by defining and analyzing longitudinal trajectories of body mass index percentile (BMIPCT) during early developmental windows. Methods: We included 3029 children from the Boston Birth Cohort (BBC) with repeated BMI measurements from birth to age 18 years. We applied locally weighted scatterplot smoothing with a time-limit scheme and predefined rules for imputation of missing data. We then used time-series K-means cluster analysis and latent class growth analysis to define longitudinal trajectories of BMIPCT from infancy up to age 18 years. Then, we investigated early life determinants of the BMI trajectories. Finally, we compared whether using early BMIPCT trajectories performs better than BMIPCT at a given age for predicting future risk of OWO. Results: After imputation, the percentage of missing data ratio decreased from 36.0% to 10.1%. We identified four BMIPCT longitudinal trajectories: early onset OWO; late onset OWO; normal stable; and low stable. Maternal OWO, smoking, and preterm birth were identified as important determinants of the two OWO trajectories. Our predictive models showed that BMIPCT trajectories in early childhood (birth to age 1 or 2 years) were more predictive of childhood OWO (age 5-10 years) than a single BMIPCT at age 1 or 2 years. Conclusions: Using longitudinal BMIPCT data from birth to age 18 years, this study identified distinct BMIPCT trajectories, examined early life determinants of these trajectories, and demonstrated their advantages in predicting childhood risk of OWO over BMIPCT at a single time point.

Association of Anti-CCAR1 Autoantibodies With Decreased Cancer Risk Relative to the General Population in Patients With Anti-Transcriptional Intermediary Factor 1γ-Positive Dermatomyositis.

OBJECTIVE: To describe the disease specificity, clinical phenotype, and risk of cancer in dermatomyositis (DM) patients with autoantibodies against cell division cycle and apoptosis regulator protein 1 (anti-CCAR1). METHODS: The frequency of anti-CCAR1 autoantibodies was measured by enzyme-linked immunosorbent assay in the serum of DM patients from 2 independent cohorts (Johns Hopkins and Stanford), with patients with several other rheumatic diseases and healthy controls used as comparators. Clinical features and the risk of cancer incidence relative to that in the general population were determined in anti-CCAR1-positive DM patients. RESULTS: Anti-CCAR1 antibodies were significantly associated with anti-transcriptional intermediary factor 1γ (anti-TIF1γ) antibodies present in the serum of patients with DM: 80 (32%) of 252 anti-TIF1γ-positive DM patients versus 14 (8%) of 186 anti-TIF1γ-negative DM patients were positive for anti-CCAR1 antibodies (P < 0.001). Anti-CCAR1 antibodies were not detected in any of the 32 serum samples from healthy controls, and were present at very low frequencies in the sera of patients with other rheumatic diseases: 1 (2.3%) of 44 patients with anti-hydroxymethylglutaryl-coenzyme A reductase-positive necrotizing myopathy, 1 (2.3%) of 44 patients with inclusion body myositis, and 3 (6.5%) of 46 patients with systemic lupus erythematosus were positive for anti-CCAR1 antibodies. Upon examining data on occurrence of cancer from the onset of DM onward, the observed number of cancers diagnosed in anti-TIF-1γ-positive DM patients was significantly greater than expected in both cohorts, with a standardized incidence ratio (SIR) of 3.49 (95% confidence interval [95% CI] 2.39-4.92) in the Johns Hopkins cohort and a SIR of 4.54 (95% CI 3.04-6.52) in the Stanford cohort (each P < 0.001). DM patients who were both anti-TIF1γ positive and anti-CCAR1 positive had lower SIRs for cancer, with a SIR of 1.78 (95% CI 0.77-3.51) (P = 0.172) in the Johns Hopkins cohort and a SIR of 1.61 (95% CI 0.44-4.13) (P = 0.48) in the Stanford cohort. CONCLUSION: Anti-CCAR1 autoantibodies are specific for anti-TIF1γ-positive DM. Their presence in anti-TIF1γ-positive patients attenuates the risk of cancer to a level comparable to that seen in the general population.

Subsets of Idiopathic Inflammatory Myositis Enriched for Contemporaneous Cancer Relative to the General Population.

OBJECTIVE: This study investigates cancer risk in idiopathic inflammatory myopathy (IIM) relative to the general population. METHODS: We conducted a single-center, retrospective cohort study of IIM patients and malignancy. Myositis-specific and -associated autoantibodies were determined by Euroimmun line blot, enzyme-linked immunosorbent assay, and immunoprecipitation. We calculated standardized prevalence ratios (SPRs) and adjusted for calendar year, age, sex, race, and ethnicity by comparing observed cancers in IIM patients versus expected cancers in the general population using the Surveillance, Epidemiology, and End Results registry. RESULTS: Of 1,172 IIM patients, 203 (17%) patients with a cancer history were studied. Over a median follow-up of 5.2 years, the observed number of IIM patients diagnosed with cancer was increased 1.43-fold (SPR 1.43 [95% confidence interval (95% CI) 1.15-1.77]; P = 0.002). Within 3 years of IIM symptom onset, an increased SPR was observed for anti-transcription intermediary factor 1γ (anti-TIF1γ)-positive patients for ovarian and breast cancer (ovarian SPR 18.39 [95% CI 5.01-47.08], P < 0.001; breast SPR 3.84 [95% CI 1.99-6.71], P < 0.001). As expected, anti-TIF1γ positivity was associated with a significantly elevated SPR; however, only 55% (36 of 66) of all cancers within 3 years of dermatomyositis onset were observed in anti-TIF1γ-positive patients. Other myositis-specific autoantibodies, including anti-Mi-2, anti-small ubiquitin-like modifier activating enzyme (SAE), and anti-nuclear matrix protein 2 (NXP-2), accounted for 26% (17 of 66) of cancers diagnosed within 3 years of dermatomyositis onset. No cancer association, positive or negative, was observed for patients with antisynthetase, anti-melanoma differentiation-associated protein 5 (anti-MDA-5), or anti-hydroxymethylglutaryl-coenzyme A reductase (anti-HMGCR) antibodies. CONCLUSION: In a tertiary referral center population, anti-TIF1γ was most strongly associated with breast and ovarian cancer. Patients with antisynthetase, anti-MDA-5, or anti-HMGCR antibodies had the same cancer risk as the general population.

DNA methylation mediates the effect of maternal smoking on offspring birthweight: a birth cohort study of multi-ethnic US mother-newborn pairs.

BACKGROUND: Maternal smoking affects more than half a million pregnancies each year in the US and is known to result in fetal growth restriction as measured by lower birthweight and its associated long-term consequences. Maternal smoking also has been linked to altered fetal DNA methylation (DNAm). However, what remains largely unexplored is whether these DNAm alterations are merely markers of smoking exposure or if they also have implications for health outcomes. This study tested the hypothesis that fetal DNAm mediates the effect of maternal smoking on newborn birthweight. METHODS: This study included mother-newborn pairs from a US predominantly urban, low-income multi-ethnic birth cohort. DNAm in cord blood were determined using the Illumina Infinium MethylationEPIC BeadChip. After standard quality control and normalization procedures, an epigenome-wide association study (EWAS) of maternal smoking was performed using linear regression models, controlling for maternal age, education, race, parity, pre-pregnancy body mass index, alcohol consumption, gestational age, maternal pregestational/gestational diabetes, child sex, cord blood cell compositions and batch effects. To quantify the degree to which cord DNAm mediates the smoking-birthweight association, the VanderWeele-Vansteelandt approach for single mediator and structural equational model for multiple mediators were used, adjusting for pertinent covariates. RESULTS: The study included 954 mother-newborn pairs. Among mothers, 165 (17.3%) ever smoked before or during pregnancy. Newborns with smoking exposure had on average 258 g lower birthweight than newborns without exposure (P < 0.001). Using a false discovery rate (FDR) < 0.05 as the significance cutoff, the EWAS identified 38 differentially methylated CpG sites associated with maternal smoking. Of those, 17 CpG sites were mapped to previously reported genes: GFI1, AHRR, CYP1A1, and CNTNAP2; 8 of those, located in the first three genes, were Bonferroni significantly associated with newborn birthweight and mediated the smoking-birthweight association. The combined mediation effect of the three genes explained 67.8% of the smoking-birthweight association. CONCLUSIONS: Our study not only lends further support that maternal smoking alters fetal DNAm in a multiethnic population, but also suggests that fetal DNAm substantially mediates the maternal smoking-birthweight association. Our findings, if further validated, indicate that DNAm modification is likely an important pathway by which maternal smoking impairs fetal growth and, perhaps, even long-term health outcomes.

COPEWELL: A Conceptual Framework and System Dynamics Model for Predicting Community Functioning and Resilience After Disasters.

OBJECTIVE: Policy-makers and practitioners have a need to assess community resilience in disasters. Prior efforts conflated resilience with community functioning, combined resistance and recovery (the components of resilience), and relied on a static model for what is inherently a dynamic process. We sought to develop linked conceptual and computational models of community functioning and resilience after a disaster. METHODS: We developed a system dynamics computational model that predicts community functioning after a disaster. The computational model outputted the time course of community functioning before, during, and after a disaster, which was used to calculate resistance, recovery, and resilience for all US counties. RESULTS: The conceptual model explicitly separated resilience from community functioning and identified all key components for each, which were translated into a system dynamics computational model with connections and feedbacks. The components were represented by publicly available measures at the county level. Baseline community functioning, resistance, recovery, and resilience evidenced a range of values and geographic clustering, consistent with hypotheses based on the disaster literature. CONCLUSIONS: The work is transparent, motivates ongoing refinements, and identifies areas for improved measurements. After validation, such a model can be used to identify effective investments to enhance community resilience. (Disaster Med Public Health Preparedness. 2018;12:127-137).