A Randomized Controlled Trial to Improve Unmet Social Needs and Clinical Outcomes Among Adults with Diabetes.

BACKGROUND: Adults with type 1 or type 2 diabetes often face financial challenges and other unmet social needs to effective diabetes self-management. OBJECTIVE: Whether a digital intervention focused on addressing socioeconomic determinants of health improves diabetes clinical outcomes more than usual care. DESIGN: Randomized trial from 2019 to 2023. PARTICIPANTS: A total of 600 adults with diabetes, HbA1c ≥ 7.5%, and self-reported unmet social needs or financial burden from a health system and randomized to the intervention or standard care. INTERVENTION: CareAvenue is an automated, e-health intervention with eight videos that address unmet social needs contributing to poor outcomes. MEASURES: Primary outcome was HbA1c, measured at baseline, and 6 and 12 months after randomization. Secondary outcomes included systolic blood pressure and reported met social needs, cost-related non-adherence (CRN), and financial burden. We examined main effects and variation in effects across predefined subgroups. RESULTS: Seventy-eight percent of CareAvenue participants completed one or more modules of the website. At 12-month follow-up, there were no significant differences in HbA1c changes between CareAvenue and control group (p = 0.24). There were also no significant between-group differences in systolic blood pressure (p = 0.29), met social needs (p = 0.25), CRN (p = 0.18), and perceived financial burden (p = 0.31). In subgroup analyses, participants with household incomes 100-400% FPL (1.93 (SE = 0.76), p < 0.01), 201-400% FPL (1.30 (SE = 0.62), p < 0.04), and > 400% FPL (1.27 (SE = 0.64), p < 0.05) had significantly less A1c decreases compared to the control group. CONCLUSIONS: On average, CareAvenue participants did not achieve better A1c lowering, met needs, CRN, or perceived financial burden compared to control participants. CareAvenue participants with higher incomes achieved significantly less A1c reductions than control. Further research is needed on social needs interventions that consider tailored approaches to population subgroups. CLINICAL TRIALS REGISTRY: ClinicalTrials.gov ID NCT03950973, May 2019.

Iron Deficiency and Vitamin D Deficiency Are Associated with Sleep in Females of Reproductive Age: An Analysis of NHANES 2005-2018 Data.

BACKGROUND: Iron and vitamin D deficiencies have been implicated in sleep disturbance. Although females are more susceptible to these deficiencies and frequently report sleep-related issues, few studies have examined these associations in females. OBJECTIVE: This study investigates the association of iron and vitamin D deficiencies on sleep in a nationally representative sample of females of reproductive age. METHODS: We used 2 samples of 20-49-y-old non-pregnant females from National Health and Nutrition Examination Survey (NHANES) 2005-2008 (N = 2497) and NHANES 2005-2010 and 2015-2018 (N = 6731) to examine the associations of iron deficiency (ID), iron deficiency anemia (IDA), vitamin D deficiency (VDD), vitamin D inadequacy (VDI), and the joint association of both deficiencies with sleep duration, latency, and quality. Sleep outcomes were measured using a self-reported questionnaire. We used the body iron model based on serum ferritin and serum soluble transferrin receptor to identify ID, along with hemoglobin to identify IDA cases. In addition, 25-hydroxyvitamin D levels were used to determine VDD and VDI cases. Logistic regression was used to evaluate these associations, adjusting for potential confounders. In addition, we assessed the multiplicative and additive interactions of both deficiencies. RESULTS: ID and IDA were associated with poor sleep quality, with 1.42 [95% confidence interval (CI): 1.02, 2.00)] and 2.08 (95% CI: 1.29, 3.38) higher odds, respectively, whereas VDD and VDI were significantly associated with short sleep duration, with 1.26 (95% CI: 1.02, 1.54) and 1.22 (95% CI: 1.04, 1.44) higher odds, respectively. Subjects with both nutritional deficiencies had significantly higher odds of poorer sleep quality compared with subjects with neither condition. For sleep quality, a significant multiplicative interaction was observed between ID and VDD (P value = 0.0005). No associations were observed between study exposures and sleep latency. CONCLUSIONS: Among females of reproductive age, iron and vitamin D deficiencies are associated with sleep health outcomes. The potential synergistic effect of both deficiencies warrants further assessment.

Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies.

Data sharing barriers present paramount challenges arising from multicenter clinical studies where multiple data sources are stored and managed in a distributed fashion at different local study sites. Merging such data sources into a common data storage for a centralized statistical analysis requires a data use agreement, which is often time-consuming. Data merging may become more burdensome when propensity score modeling is involved in the analysis because combining many confounding variables, and systematic incorporation of this additional modeling in a meta-analysis has not been thoroughly investigated in the literature. Motivated from a multicenter clinical trial of basal insulin treatment for reducing the risk of post-transplantation diabetes mellitus, we propose a new inference framework that avoids the merging of subject-level raw data from multiple sites at a centralized facility but needs only the sharing of summary statistics. Unlike the architecture of federated learning, the proposed collaborative inference does not need a center site to combine local results and thus enjoys maximal protection of data privacy and minimal sensitivity to unbalanced data distributions across data sources. We show theoretically and numerically that the new distributed inference approach has little loss of statistical power compared to the centralized method that requires merging the entire data. We present large-sample properties and algorithms for the proposed method. We illustrate its performance by simulation experiments and the motivating example on the differential average treatment effect of basal insulin to lower risk of diabetes among kidney-transplant patients compared to the standard-of-care.

Adaptive bootstrap tests for composite null hypotheses in the mediation pathway analysis.

Mediation analysis aims to assess if, and how, a certain exposure influences an outcome of interest through intermediate variables. This problem has recently gained a surge of attention due to the tremendous need for such analyses in scientific fields. Testing for the mediation effect (ME) is greatly challenged by the fact that the underlying null hypothesis (i.e. the absence of MEs) is composite. Most existing mediation tests are overly conservative and thus underpowered. To overcome this significant methodological hurdle, we develop an adaptive bootstrap testing framework that can accommodate different types of composite null hypotheses in the mediation pathway analysis. Applied to the product of coefficients test and the joint significance test, our adaptive testing procedures provide type I error control under the composite null, resulting in much improved statistical power compared to existing tests. Both theoretical properties and numerical examples of the proposed methodology are discussed.

Longitudinal self-learning of individualized treatment rules in a nutrient supplementation trial with missing data.

Longitudinal outcomes are prevalent in clinical studies, where the presence of missing data may make the statistical learning of individualized treatment rules (ITRs) a much more challenging task. We analyzed a longitudinal calcium supplementation trial in the ELEMENT Project and established a novel ITR to reduce the risk of adverse outcomes of lead exposure on child growth and development. Lead exposure, particularly in the form of in utero exposure, can seriously impair children's health, especially their cognitive and neurobehavioral development, which necessitates clinical interventions such as calcium supplementation intake during pregnancy. Using the longitudinal outcomes from a randomized clinical trial of calcium supplementation, we developed a new ITR for daily calcium intake during pregnancy to mitigate persistent lead exposure in children at age 3 years. To overcome the technical challenges posed by missing data, we illustrate a new learning approach, termed longitudinal self-learning (LS-learning), that utilizes longitudinal measurements of child's blood lead concentration in the derivation of ITR. Our LS-learning method relies on a temporally weighted self-learning paradigm to synergize serially correlated training data sources. The resulting ITR is the first of this kind in precision nutrition that will contribute to the reduction of expected blood lead concentration in children aged 0-3 years should this ITR be implemented to the entire study population of pregnant women.

Cross-sectional associations between phthalates, phenols, and parabens with metabolic syndrome risk during early-to-mid adolescence among a cohort of Mexican youth.

BACKGROUND: Epidemiological studies on children and adults have linked toxicants from plastics and personal care products to metabolic disruption. Yet, the impact of endocrine-disrupting chemicals (EDCs) on adolescent metabolic syndrome (MetS) risk during early and mid-adolescence is unclear. METHODS: To examine the links between exposure to EDCs and MetS risk and its components, cross-sectional data from 344 Mexican youth in early-to-mid adolescence (10-17 years) were analyzed. Urinary biomarker concentrations of phthalates, phenol, and paraben analytes were measured from a single spot urine sample collected in 2015; study personnel obtained anthropometric and metabolic measures. We examined associations between summary phthalates and metabolites, phenol, and paraben analytes with MetS risk z-scores using linear regression, adjusted for specific gravity, sex, age, pubertal status, smoking, alcohol intake, physical activity level, and screen time. As a secondary aim, mediation analysis was conducted to evaluate the role of hormones in the association between summary phthalates with lipids and MetS risk z-scores. RESULTS: The mean (SD) age was 13.2 (1.9) years, and 50.9% were female. Sex-stratified analyses revealed associations between summary phthalates and lipids ratio z-scores, including Σ DEHP [ß = 0.21 (95% CI: 0.04, 0.37; p < 0.01)], phthalates from plastic sources (Σ Plastic) [ß = 0.22 (95% CI: 0.05, 0.39; p < 0.01)], anti-androgenic phthalates (Σ AA) [ß = 0.22 (95% CI: 0.05, 0.39; p < 0.01)], and individual phthalate metabolites (MEHHP, MEOHP, and MECPP) among males. Among females, BPA [ß = 0.24 (95% CI: 0.03, 0.44; p < 0.05)] was positively associated with lipids ratio z-score and one phenol (2,5 DCP) [ß = 0.09 (95% CI: 0.01, 0.18); p < 0.05)] was associated with increased waist circumference z-score. Results showed no evidence of mediation by hormone concentrations in the association between summary phthalates with lipids ratio or MetS risk z-scores. CONCLUSION: Higher EDC exposure was positively associated with serum lipids during adolescence, particularly among males.

Third-Trimester Maternal Dietary Patterns Are Associated with Sleep Health among Adolescent Offspring in a Mexico City Cohort.

BACKGROUND: Maternal diet during gestation has been linked to infant sleep; whether associations persist through adolescence is unknown. OBJECTIVES: We explored associations between trimester-specific maternal diet patterns and measures of sleep health among adolescent offspring in a Mexico City birth cohort. METHODS: Data from 310 mother-adolescent dyads were analyzed. Maternal diet patterns were identified by principal component analysis derived from FFQs collected during each trimester of pregnancy. Sleep duration, midpoint, and fragmentation were obtained from 7-d actigraphy data when adolescents were between 12 and 20 y old. Unstratified and sex-stratified association analyses were conducted using linear regression models, adjusted for potential confounders. RESULTS: Mean ± SD age of offspring was 15.1 ± 1.9 y, and 52.3% of the sample was female. Three diet patterns were identified during each trimester of pregnancy: the Prudent Diet (PD), high in lean proteins and vegetables; the Transitioning Mexican Diet (TMD), high in westernized foods; and the High Meat & Fat Diet (HMFD), high in meats and fat products. Mean ± SD sleep duration was 8.5 ± 1.5 h/night. Most associations were found in the third trimester. Specifically, PD maternal adherence was associated with shorter sleep duration among offspring (-0.57 h; 95% CI: -0.98, -0.16 h, in the highest tertile compared with the lowest) and earlier sleep midpoint among females (-0.77 h; 95% CI: -1.3, -0.26 h). Adherence to the HMFD and TMD was nonlinearly associated with less fragmented sleep, with the latter only evident among females. CONCLUSIONS: Findings indicate that maternal dietary patterns, especially during the third trimester of pregnancy, may have long-term impacts on offspring sleep.

Domain-specific effects of prenatal fluoride exposure on child IQ at 4, 5, and 6-12 years in the ELEMENT cohort.

OBJECTIVE: Prenatal exposure to fluoride has been associated with adverse neurodevelopmental outcomes. However, the neuropsychological profile of fluoride's developmental neurotoxicity at low levels and the stability of this relationship across childhood has not been characterized. We investigated the longitudinal and domain specific effect of prenatal fluoride exposure on IQ among children ages 4, 5, and 6-12 years in the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) cohort. METHODS: We measured the average of maternal urinary fluoride at each trimester of pregnancy adjusted for creatinine (MUFCRE). Children were administered the McCarthy Scales of Children's Abilities at ages 4 (N = 386) and 5 (N = 308), and the Wechsler Abbreviated Scale of Intelligence at age 6-12 (N = 278). We used generalized estimating equation (GEE) models to estimate the population averaged effect of MUFCRE concentration on longitudinal General Cognitive Index (GCI)/Full-Scale IQ (FSIQ), Verbal IQ (VIQ), and Performance IQ (PIQ) scores (N = 348). We tested for possible interactions between MUFCRE and child sex as well as for MUFCRE and time point on children's IQ. All models controlled for relevant available covariates. RESULTS: The mean/median MUFCRE concentration was 0.90/0.83 mg/L (SD = 0.39; IQR, 0.64-1.11 mg/L). A 0.5 mg/L increase in MUFCRE predicted an average 2.12-point decrease in GCI/FSIQ (95% CI: -3.49, -0.75) and 2.63-point decrease in PIQ (95% CI: -3.87, -1.40). MUFCRE was marginally associated with VIQ across time (B = -1.29, 95% CI: -2.60, 0.01). No interactions between MUFCRE and child sex or MUFCRE and time were observed. CONCLUSION: The negative association between prenatal fluoride exposure and longitudinal IQ was driven by decrements in non-verbal intelligence (i.e. PIQ), suggesting that visual-spatial and perceptual reasoning abilities may be more impacted by prenatal fluoride exposure as compared to verbal abilities.

Sleep Difficulties among Mexican Adolescents: Subjective and Objective Assessments of Sleep.

OBJECTIVE/BACKGROUND: Self-reported sleep difficulties, such as insomnia symptoms, have been reported among adolescents. Yet, studies of their prevalence and correlates are scarce among Latin Americans. This study sought (1) to describe associations between sociodemographic and lifestyle factors with self-reported sleep difficulties and (2) to examine associations between self-reported sleep difficulties and actigraphy-based sleep. PARTICIPANTS: Participants included 477 Mexican adolescents from the ELEMENT cohort. METHODS: Over 7 days, self-reported sleep measures (hard time falling asleep, overall sleep difficulties, and specific types of sleep difficulties) were obtained from daily sleep diaries. Actigraphy-based sleep measures (duration, i.e. sleep onset to morning wake, midpoint, and fragmentation) were concurrently assessed using a wrist actigraph. RESULTS: Mean (SD) age was 15.9 (2.2) years, and 53.5% were females. Mean (SD) sleep duration was 8.5 (1.2) h/night. Half reported a hard time falling asleep at least 3 days, and 25% had sleep difficulties at least 3 days over 7 days. The 3 types of sleep difficulties commonly reported among the entire cohort were insomnia/restlessness (29%), environmental (27%), and mental/emotional difficulties (19%). Female sex, smoking behavior, and socioeconomic indicators were among the most consistent factors associated with sleep difficulties. Subjective sleep difficulties were associated with shorter sleep duration (ß = -20.8 [-35.3, -6.2] min), while subjective hard time falling asleep was associated with longer sleep duration (ß = 11.3 [4.6, 27.2] min). CONCLUSION: A high proportion of Mexican adolescents in the sample reported sleep difficulties. Findings demonstrate the importance of obtaining subjective and objective sleep measures for a more comprehensive assessment of adolescent sleep.

A Benchmark Dose Analysis for Maternal Pregnancy Urine-Fluoride and IQ in Children.

As a guide to establishing a safe exposure level for fluoride exposure in pregnancy, we applied benchmark dose modeling to data from two prospective birth cohort studies. We included mother-child pairs from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) cohort in Mexico and the Maternal-Infant Research on Environmental Chemicals (MIREC) cohort in Canada. Maternal urinary fluoride concentrations (U-F, in mg/L, creatinine-adjusted) were measured in urine samples obtained during pregnancy. Children were assessed for intelligence quotient (IQ) at age 4 (n = 211) and between six and 12 years (n = 287) in the ELEMENT cohort, and three to four years (n = 407) in the MIREC cohort. We calculated covariate-adjusted regression coefficients and their standard errors to assess the association of maternal U-F concentrations with children's IQ measures. Assuming a benchmark response of 1 IQ point, we derived benchmark concentrations (BMCs) and benchmark concentration levels (BMCLs). No deviation from linearity was detected in the dose-response relationships, but boys showed lower BMC values than girls. Using a linear slope for the joint cohort data, the BMC for maternal U-F associated with a 1-point decrease in IQ scores was 0.31 mg/L (BMCL, 0.19 mg/L) for the youngest boys and girls in the two cohorts, and 0.33 mg/L (BMCL, 0.20 mg/L) for the MIREC cohort and the older ELEMENT children. Thus, the joint data show a BMCL in terms of the adjusted U-F concentrations in the pregnant women of approximately 0.2 mg/L. These results can be used to guide decisions on preventing excess fluoride exposure in pregnant women.

Early Postoperative Basal Insulin Therapy versus Standard of Care for the Prevention of Diabetes Mellitus after Kidney Transplantation: A Multicenter Randomized Trial.

BACKGROUND: Post-transplantation diabetes mellitus (PTDM) might be preventable. METHODS: This open-label, multicenter randomized trial compared 133 kidney transplant recipients given intermediate-acting insulin isophane for postoperative afternoon glucose ≥140 mg/dl with 130 patients given short-acting insulin for fasting glucose ≥200 mg/dl (control). The primary end point was PTDM (antidiabetic treatment or oral glucose tolerance test-derived 2 hour glucose ≥200 mg/dl) at month 12 post-transplant. RESULTS: In the intention-to-treat population, PTDM rates at 12 months were 12.2% and 14.7% in treatment versus control groups, respectively (odds ratio [OR], 0.82; 95% confidence interval [95% CI], 0.39 to 1.76) and 13.4% versus 17.4%, respectively, at 24 months (OR, 0.71; 95% CI, 0.34 to 1.49). In the per-protocol population, treatment resulted in reduced odds for PTDM at 12 months (OR, 0.40; 95% CI, 0.16 to 1.01) and 24 months (OR, 0.54; 95% CI, 0.24 to 1.20). After adjustment for polycystic kidney disease, per-protocol ORs for PTDM (treatment versus controls) were 0.21 (95% CI, 0.07 to 0.62) at 12 months and 0.35 (95% CI, 0.14 to 0.87) at 24 months. Significantly more hypoglycemic events (mostly asymptomatic or mildly symptomatic) occurred in the treatment group versus the control group. Within the treatment group, nonadherence to the insulin initiation protocol was associated with significantly higher odds for PTDM at months 12 and 24. CONCLUSIONS: At low overt PTDM incidence, the primary end point in the intention-to-treat population did not differ significantly between treatment and control groups. In the per-protocol analysis, early basal insulin therapy resulted in significantly higher hypoglycemia rates but reduced odds for overt PTDM-a significant reduction after adjustment for baseline differences-suggesting the intervention merits further study.Clinical Trial registration number: NCT03507829.

Multivariate Functional Kernel Machine Regression and Sparse Functional Feature Selection.

Motivated by mobile devices that record data at a high frequency, we propose a new methodological framework for analyzing a semi-parametric regression model that allow us to study a nonlinear relationship between a scalar response and multiple functional predictors in the presence of scalar covariates. Utilizing functional principal component analysis (FPCA) and the least-squares kernel machine method (LSKM), we are able to substantially extend the framework of semi-parametric regression models of scalar responses on scalar predictors by allowing multiple functional predictors to enter the nonlinear model. Regularization is established for feature selection in the setting of reproducing kernel Hilbert spaces. Our method performs simultaneously model fitting and variable selection on functional features. For the implementation, we propose an effective algorithm to solve related optimization problems in that iterations take place between both linear mixed-effects models and a variable selection method (e.g., sparse group lasso). We show algorithmic convergence results and theoretical guarantees for the proposed methodology. We illustrate its performance through simulation experiments and an analysis of accelerometer data.

A Pattern Dictionary Method for Anomaly Detection.

In this paper, we propose a compression-based anomaly detection method for time series and sequence data using a pattern dictionary. The proposed method is capable of learning complex patterns in a training data sequence, using these learned patterns to detect potentially anomalous patterns in a test data sequence. The proposed pattern dictionary method uses a measure of complexity of the test sequence as an anomaly score that can be used to perform stand-alone anomaly detection. We also show that when combined with a universal source coder, the proposed pattern dictionary yields a powerful atypicality detector that is equally applicable to anomaly detection. The pattern dictionary-based atypicality detector uses an anomaly score defined as the difference between the complexity of the test sequence data encoded by the trained pattern dictionary (typical) encoder and the universal (atypical) encoder, respectively. We consider two complexity measures: the number of parsed phrases in the sequence, and the length of the encoded sequence (codelength). Specializing to a particular type of universal encoder, the Tree-Structured Lempel-Ziv (LZ78), we obtain a novel non-asymptotic upper bound, in terms of the Lambert W function, on the number of distinct phrases resulting from the LZ78 parser. This non-asymptotic bound determines the range of anomaly score. As a concrete application, we illustrate the pattern dictionary framework for constructing a baseline of health against which anomalous deviations can be detected.

Deceased donors as nondirected donors in kidney paired donation.

As proof of concept, we simulate a revised kidney allocation system that includes deceased donor (DD) kidneys as chain-initiating kidneys (DD-CIK) in a kidney paired donation pool (KPDP), and estimate potential increases in number of transplants. We consider chains of length 2 in which the DD-CIK gives to a candidate in the KPDP, and that candidate's incompatible donor donates to theDD waitlist. In simulations, we vary initial pool size, arrival rates of candidate/donor pairs and (living) nondirected donors (NDDs), and delay time from entry to the KPDP until a candidate is eligible to receive a DD-CIK. Using data on candidate/donor pairs and NDDs from the Alliance for Paired Kidney Donation, and the actual DDs from the Scientific Registry of Transplant Recipients (SRTR) data, simulations extend over 2 years. With an initial pool of 400, respective candidate and NDD arrival rates of 2 per day and 3 per month, and delay times for access to DD-CIK of 6 months or less, including DD-CIKs increases the number of transplants by at least 447 over 2 years, and greatly reduces waiting times of KPDP candidates. Potential effects on waitlist candidates are discussed as are policy and ethical issues.

Structural factor equation models for causal network construction via directed acyclic mixed graphs.

Directed acyclic mixed graphs (DAMGs) provide a useful representation of network topology with both directed and undirected edges subject to the restriction of no directed cycles in the graph. This graphical framework may arise in many biomedical studies, for example, when a directed acyclic graph (DAG) of interest is contaminated with undirected edges induced by some unobserved confounding factors (eg, unmeasured environmental factors). Directed edges in a DAG are widely used to evaluate causal relationships among variables in a network, but detecting them is challenging when the underlying causality is obscured by some shared latent factors. The objective of this paper is to develop an effective structural equation model (SEM) method to extract reliable causal relationships from a DAMG. The proposed approach, termed structural factor equation model (SFEM), uses the SEM to capture the network topology of the DAG while accounting for the undirected edges in the graph with a factor analysis model. The latent factors in the SFEM enable the identification and removal of undirected edges, leading to a simpler and more interpretable causal network. The proposed method is evaluated and compared to existing methods through extensive simulation studies, and illustrated through the construction of gene regulatory networks related to breast cancer.

Net benefit index: Assessing the influence of a biomarker for individualized treatment rules.

One central task in precision medicine is to establish individualized treatment rules (ITRs) for patients with heterogeneous responses to different therapies. Motivated from a randomized clinical trial for Type 2 diabetic patients on a comparison of two drugs, that is, pioglitazone and gliclazide, we consider a problem: utilizing promising candidate biomarkers to improve an existing ITR. This calls for a biomarker evaluation procedure that enables to gauge added values of individual biomarkers. We propose an assessment analytic, termed as net benefit index (NBI), that quantifies a contrast between the resulting gain and loss of treatment benefits when a biomarker enters ITR to reallocate patients in treatments. We optimize reallocation schemes via outcome weighted learning (OWL), from which the optimal treatment group labels are generated by weighted support vector machine (SVM). To account for sampling uncertainty in assessing a biomarker, we propose an NBI-based test for a significant improvement over the existing ITR, where the empirical null distribution is constructed via the method of stratified permutation by treatment arms. Applying NBI to the motivating diabetes trial, we found that baseline fasting insulin is an important biomarker that leads to an improvement over an existing ITR based only on patient's baseline fasting plasma glucose (FPG), age, and body mass index (BMI) to reduce FPG over a period of 52 weeks.

Poststratification fusion learning in longitudinal data analysis.

Stratification is a very commonly used approach in biomedical studies to handle sample heterogeneity arising from, for examples, clinical units, patient subgroups, or missing-data. A key rationale behind such approach is to overcome potential sampling biases in statistical inference. Two issues of such stratification-based strategy are (i) whether individual strata are sufficiently distinctive to warrant stratification, and (ii) sample size attrition resulted from the stratification may potentially lead to loss of statistical power. To address these issues, we propose a penalized generalized estimating equations approach to reducing the complexity of parametric model structures due to excessive stratification. Specifically, we develop a data-driven fusion learning approach for longitudinal data that improves estimation efficiency by integrating information across similar strata, yet still allows necessary separation for stratum-specific conclusions. The proposed method is evaluated by simulation studies and applied to a motivating example of psychiatric study to demonstrate its usefulness in real world settings.

A binary hidden Markov model on spatial network for amyotrophic lateral sclerosis disease spreading pattern analysis.

Amyotrophic lateral sclerosis (ALS) is a neurological disease that starts at a focal point and gradually spreads to other parts of the nervous system. One of the main clinical symptoms of ALS is muscle weakness. To study spreading patterns of muscle weakness, we analyze spatiotemporal binary muscle strength data, which indicates whether observed muscle strengths are impaired or healthy. We propose a hidden Markov model-based approach that assumes the observed disease status depends on two latent disease states. The model enables us to estimate the incidence rate of ALS disease and the probability of disease state transition. Specifically, the latter is modeled by a logistic autoregression in that the spatial network of susceptible muscles follows a Markov process. The proposed model is flexible to allow both historical muscle conditions and their spatial relationships to be included in the analysis. To estimate the model parameters, we provide an iterative algorithm to maximize sparse-penalized likelihood with bias correction, and use the Viterbi algorithm to label hidden disease states. We apply the proposed approach to analyze the ALS patients' data from EMPOWER Study.

A behavioral intervention can decrease asthma exacerbations in older adults.

BACKGROUND: Older adults have higher rates of asthma morbidity and mortality compared with younger age groups. Few interventions are tailored to this population. OBJECTIVE: To evaluate a self-management asthma intervention in older adults. METHODS: Adults age 55 and older with persistent asthma were enrolled into blinded, randomized controlled trial of a 6-session asthma self-management intervention. This educational intervention was conducted in group sessions and through individual telephone calls. Outcomes including asthma exacerbations (defined as unscheduled office visits, emergency department visits, or hospitalizations for asthma), spirometric values, FeNO, asthma control, asthma quality of life, and asthma self-management were assessed at 3, 6, and 12 months. RESULTS: One hundred eighty-nine subjects were enrolled, 172 were randomized and received at least 1 treatment dose, and 145 (84%) were analyzed at 12 months. On a modified intent-to-treat analysis, those in the intervention group were less likely to have an asthma exacerbation (26.9% vs 47.1%, P = .01), had a lower asthma exacerbation rate (0.8 vs 1.9, P = .02), had better asthma control (19.9 vs 18.6, P = .08), and had a higher asthma self-management score (8.9 vs 8.4, P = .03). After a mixed-model analysis to control for confounding factors, a decrease in asthma exacerbations (P = .02), as well as a decreased asthma exacerbation rate (P = .04), remained statistically significant, whereas asthma control and self-management did not. No other significant outcome differences were found. CONCLUSION: A 6-session asthma self-management intervention can successfully decrease asthma exacerbations among older adults.

Mercury exposure in relation to sleep duration, timing, and fragmentation among adolescents in Mexico City.

INTRODUCTION: Mercury intoxication is known to be associated with adverse symptoms of fatigue and sleep disturbances, but whether low-level mercury exposure could affect sleep remains unclear. In particular, children may be especially vulnerable to both mercury exposures and to poor sleep. We sought to examine associations between mercury levels and sleep disturbances in Mexican youth. METHODS: The study sample comprised 372 youth from the Early Life Exposures to Environmental Toxicants (ELEMENT) cohort, a birth cohort from Mexico City. Sleep (via 7-day actigraphy) and concurrent urine mercury were assessed during a 2015 follow-up visit. Mercury was also assessed in mid-childhood hair, blood, and urine during an earlier study visit, and was considered a secondary analysis. We used linear regression and varying coefficient models to examine non-linear associations between Hg exposure biomarkers and sleep duration, timing, and fragmentation. Unstratified and sex-stratified analyses were adjusted for age and maternal education. RESULTS: During the 2015 visit, participants were 13.3 ± 1.9 years, and 48% were male. There was not a cross-sectional association between urine Hg and sleep characteristics. In secondary analysis using earlier biomarkers of Hg, lower and higher blood Hg exposure was associated with longer sleep duration among girls only. In both boys and girls, Hg biomarker levels in 2008 were associated with later adolescent sleep midpoint (for Hg urine in girls, and for blood Hg in boys). For girls, each unit log Hg was associated with 0.2 h later midpoint (95% CI 0 to 0.4), and for boys each unit log Hg was associated with a 0.4 h later sleep midpoint (95% CI 0.1 to 0.8). CONCLUSIONS: There were mostly null associations between Hg exposure and sleep characteristics among Mexican children. Yet, in both boys and girls, higher Hg exposure in mid-childhood (measured in urine and blood, respectively) was related to later sleep timing in adolescence.