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Background Overweight, obesity, and associated comorbidities are a pressing global issue among children of all ages, particularly among low-income populations. Rapid weight gain (RWG) in the first 6 months of infancy contributes to childhood obesity. Suboptimal sleep-wake patterns and gut microbiota (GM) have also been associated with childhood obesity, but little is known about their influences on early infant RWG. Sleep may alter the GM and infant metabolism, and ultimately impact obesity; however, data on the interaction between sleep-wake patterns and GM development on infant growth are scarce. In this study, we aim to investigate associations of infant sleep-wake patterns and GM development with RWG at 6 months and weight gain at 12 months. We also aim to evaluate whether temporal interactions exist between infant sleep-wake patterns and GM, and if these relations influence RWG. Methods The Snuggle Bug/ Acurrucadito study is an observational, longitudinal study investigating whether 24-h, actigraphy-assessed, sleep-wake patterns and GM development are associated with RWG among infants in their first year. Based on the Ecological Model of Growth, we propose a novel conceptual framework to incorporate sleep-wake patterns and the GM as metabolic contributors for RWG in the context of maternal-infant interactions, and familial and socio-physical environments. In total, 192 mother-infant pairs will be recruited, and sleep-wake patterns and GM development assessed at 3 and 8 weeks, and 3, 6, 9, and 12 months postpartum. Covariates including maternal and child characteristics, family and environmental factors, feeding practices and dietary intake of infants and mothers, and stool-derived metabolome and exfoliome data will be assessed. The study will apply machine learning techniques combined with logistic time-varying effect models to capture infant growth and aid in elucidating the dynamic associations between study variables and RWG. Discussion Repeated, valid, and objective assessment at clinically and developmentally meaningful intervals will provide robust measures of longitudinal sleep, GM, and growth. Project findings will provide evidence for future interventions to prevent RWG in infancy and subsequent obesity. The work also may spur the development of evidence-based guidelines to address modifiable factors that influence sleep-wake and GM development and prevent childhood obesity.

BackgroundThere has been an increasing recognition of the influence of social, behavioral, economic, and environmental factors on overall patient health. The purpose of this project was to leverage the ICD-10 codes to identify and link social determinants of health (SDoH) to patients with a high probability of utilizing acute care services and to determine if social service intervention reduced care utilization.MethodsWe analyzed retrospective data for active patients at a Department of Veterans Affairs Medical Center (VAMC) from 2015-2017. Eleven categories of SDoH were developed based on existing literature of the social determinants; the relevant ICD-10 codes were divided among these categories. Emergency Room (ER) visits, hospital admissions, and social work visits were determined for each patient in the cohort.ResultsIn a cohort of 44,401 patients, the presence of ICD-10 codes within the EHR in the 11 SDoH categories was positively correlated with increased acute care utilization. Veterans with at least one SDoH risk factor were 71% (95%CI: 68% - 75%) more likely to use the ED and 71% (95%CI: 65%-77%) more likely to be admitted to the hospital. Utilization decreased with social service interventions.ConclusionThis project demonstrates a potentially meaningful method to capture patient social risk profiles through existing EHR data in the form of ICD-10 codes, which can be used to identify the highest risk patients for intervention with the understanding that not all SDoH codes are uniformly used and some SDoHs may not be captured.

Computer simulations have been used to model infectious diseases to examine the outcomes of alternative strategies for managing their spread. Methicillin resistant Staphylococcus aureus (MRSA) skin and soft tissue infections have become prominent in many communities and efforts are underway to reduce the spread of this organism both in hospitals and communities. Currently, there are few tools for policy makers to use to examine the outcome of various choices when making decisions about MRSA. Using the example of MRSA, we describe, in this paper, a rigorous approach for development and validation of a tool that simulates the spread of MRSA infections. We used sensitivity analyses in a novel way and validated the simulation results against local data over time. Our approach for simulation development and validation is generalizeable to simulations of other diseases.

Context: Information systems are used by most states to maintain registries of immunization data both for monitoring population-level adherence and for use in clinical practice and research. Direct data exchange between such systems and electronic health record systems presents an opportunity to improve the completeness and quality of information available. Objective: Our goals were to describe and compare the completeness of the Arizona State Immunization System, the electronic health record at a large community health provider in Arizona exchanging electronic data with the Arizona system, and personal immunization records in an effort to contribute to the discussion on the completeness of state-run immunization registries and data exchange with these registries. Design: Immunization histories from these sources were collected and reviewed sequentially. Unique dates of vaccination administrations were counted for each patient and tagged on the basis of comparisons across sources. Results: We quantified completeness by combining information from all 3 sources and comparing each source with the complete set. We determined that the state registry was 71.8% complete, the hospital electronic health record was 81.9% complete, and personal records were 87.8% complete. Of the 2017 unique vaccination administrations, 65% were present in all 3 sources, 24.6% in 2 of the 3 sources, and 10.4% in only 1 source. Only 11% of patients had records in complete agreement across the 3 sources. Conclusion: This study highlights issues related to data completeness, exchange, and reporting of immunization information to state registries and suggests that there is some degree of deficiency in completeness of immunization registries and other sources. This study indicates that there is a need to strengthen links between electronic data sources with immunization information and describes potential improvements in completeness that such efforts could provide, enabling providers to better rely on state immunization registries and to improve research utilization of immunization information systems.

Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) has become a major cause of skin and soft tissue infections (SSTIs) in the US. We developed an age-structured compartmental model to study the spread of CA-MRSA at the population level and assess the effect of control intervention strategies. We used Monte-Carlo Markov Chain (MCMC) techniques to parameterize our model using monthly time series data on SSTIs incidence in children (≤19 years) during January 2004 -December 2006 in Maricopa County, Arizona. Our model-based forecast for the period January 2007–December 2008 also provided a good fit to data. We also carried out an uncertainty and sensitivity analysis on the control reproduction number, which we estimated at 1.3 (95% CI [1.2,1.4]) based on the model fit to data. Using our calibrated model, we evaluated the effect of typical intervention strategies namely reducing the contact rate of infected individuals owing to awareness of infection and decolonization strategies targeting symptomatic infected individuals on both and the long-term disease dynamics. We also evaluated the impact of hypothetical decolonization strategies targeting asymptomatic colonized individuals. We found that strategies focused on infected individuals were not capable of achieving disease control when implemented alone or in combination. In contrast, our results suggest that decolonization strategies targeting the pediatric population colonized with CA-MRSA have the potential of achieving disease elimination., Author Summary Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is a bacteria that causes skin infections in the US. We developed a mathematical model of CA-MRSA transmission among different age groups at the population level. We parameterized the model using monthly time series data on number of SSTIs in children during the period January 2004–December 2006 in Maricopa County, Arizona. Our model-based forecast to additional time series data covering the period 2007–2008 yielded a good fit to data. Using our calibrated model, we calculated that an infected individual generates on average 1.3 infected people in a totally susceptible population in the study area. We assessed the impact of intervention strategies including reductions in contact rates between infected and non-infected individuals and the effect of decolonization strategies aimed at infected individuals by drug treatment, and found that neither of the two strategies when implemented alone or in combination were able to control the disease. In contrast, we found that decolonization strategies targeting the pediatric population colonized with CA-MRSA have the potential of achieving disease elimination.

Methicillin resistant Staphylococcus aureus (MRSA) is currently a major cause of skin and soft tissue infections (SSTI) in the United States. Seasonal variation of MRSA infections in hospital settings has been widely observed. However, systematic time-series analysis of incidence data is desirable to understand the seasonality of community acquired (CA)-MRSA infections at the population level. In this paper, using data on monthly SSTI incidence in children aged 0–19 years and enrolled in Medicaid in Maricopa County, Arizona, from January 2005 to December 2008, we carried out time-series and nonlinear regression analysis to determine the periodicity, trend, and peak timing in SSTI incidence in children at different age: 0–4 years, 5–9 years, 10–14 years, and 15–19 years. We also assessed the temporal correlation between SSTI incidence and meteorological variables including average temperature and humidity. Our analysis revealed a strong annual seasonal pattern of SSTI incidence with peak occurring in early September. This pattern was consistent across age groups. Moreover, SSTIs followed a significantly increasing trend over the 4-year study period with annual incidence increasing from 3.36% to 5.55% in our pediatric population of approximately 290,000. We also found a significant correlation between the temporal variation in SSTI incidence and mean temperature and specific humidity. Our findings could have potential implications on prevention and control efforts against CA-MRSA.