Combined impact of dynamic air pollution and transportation noise on cardiometabolic disorders in San Diego County.

Air pollution and noise exposure may synergistically contribute to increased cardiometabolic disorders; however, few studies have examined this potential interaction nor considered exposures beyond residential location. This study investigates the combined impact of dynamic air pollution and transportation noise on cardiometabolic disorders in San Diego County. Using the Community of Mine Study (2014-2017), 602 ethnically diverse participants were assessed for obesity, dyslipidemia, hypertension, and metabolic syndrome (MetS) using anthropometric measurements and biomarkers from blood samples. Time-weighted measures of exposure to PM2.5, NO2, road and aircraft noise were calculated using global positioning system (GPS) mobility data and Kernel Density Estimation. Generalized estimating equation models were used to analyze associations. Interactions were assessed on the multiplicative and additive scales using relative excess risk due to interaction (RERI). We found that air pollution and noise interact to affect metabolic disorders on both multiplicative and additive scales. The effect of noise on obesity and MetS was higher when air pollution was higher. The RERI of aircraft noise and NO2 on obesity and MetS were 0.13 (95%CI 0.03, 0.22) and 0.13 (95%CI 0.02, 0.25), respectively. This finding suggests that aircraft noise and air pollution may have synergistic effects on obesity and MetS.

Designing and Evaluating a Hierarchical Framework for Matching Food Outlets across Multi-sourced Geospatial Datasets: a Case Study of San Diego County.

Research on retail food environment (RFE) relies on data availability and accuracy. However, the discrepancies in RFE datasets may lead to imprecision when measuring association with health outcomes. In this research, we present a two-tier hierarchical point of interest (POI) matching framework to compare and triangulate food outlets across multiple geospatial data sources. Two matching parameters were used including the geodesic distance between businesses and the similarity of business names according to Levenshtein distance (LD) and Double Metaphone (DM). Sensitivity analysis was conducted to determine thresholds of matching parameters. Our Tier 1 matching used more restricted parameters to generate high confidence-matched POIs, whereas in Tier 2 we opted for relaxed matching parameters and applied a weighted multi-attribute model on the previously unmatched records. Our case study in San Diego County, California used government, commercial, and crowdsourced data and returned 20.2% matched records from Tier 1 and 18.6% matched from Tier 2. Our manual validation shows a 100% matching rate for Tier 1 and up to 30.6% for Tier 2. Matched and unmatched records from Tier 1 were further analyzed for spatial patterns and categorical differences. Our hierarchical POI matching framework generated highly confident food POIs by conflating datasets and identified some food POIs that are unique to specific data sources. Triangulating RFE data can reduce uncertain and invalid POI listings when representing food environment using multiple data sources. Studies investigating associations between food environment and health outcomes may benefit from improved quality of RFE.

Exploring the impact of environmental exposure changes on metabolic biomarkers: A 6-month GPS-GIS study among women with overweight or obesity.

BACKGROUND: Little is known about the impact of environmental exposure change on metabolic biomarkers associated with cancer risk. Furthermore, this limited epidemiological evidence on metabolic biomarkers focused on residential exposure, without considering the activity space which can be done by modelling dynamic exposures. In this longitudinal study, we aimed to investigate the impact of environmental exposures change on metabolic biomarkers using GPS-GIS based measurements. METHODS: Among two weight loss interventions, the Reach for Health and the MENU studies, which included ∼460 women at risk of breast cancer or breast cancer survivors residing in Southern California, three metabolic biomarkers (insulin resistance, fasting glucose, and C-reactive protein) were assessed. Dynamic GPS-GIS based exposure to green spaces, recreation, walkability, NO2, and PM2.5 were calculated at baseline and 6 months follow-up using time-weighted spatial averaging. Generalized estimating equations models were used to examine the relationship between changes in environmental exposures and biomarker levels over time. RESULTS: Overall, six-month environmental exposure change was not associated with metabolic biomarkers change. Stratified analyses by level of environmental exposures at baseline revealed that reduced NO2 and PM2.5 exposure was associated with reduced fasting glucose concentration among women living in a healthier environment at baseline (ß -0.010, 95%CI -0.025, 0.005; ß -0.019, 95%CI -0.034, -0.003, respectively). Women living in poor environmental conditions at baseline and exposed to greener environments had decreased C-reactive protein concentrations (ß -1.001, 95%CI -1.888, -0.131). CONCLUSIONS: The impact of environmental exposure changes on metabolic biomarkers over time may be modified by baseline exposure conditions.

Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE).

BACKGROUND/OBJECTIVES: Sedentary behavior (SB) has both movement and postural components, but most SB research has only assessed low movement, especially in children. The purpose of this study was to compare estimates and health associations of SB when derived from a standard accelerometer cut-point, a novel sitting detection technique (CNN Hip Accelerometer Posture for Children; CHAP-Child), and both combined. METHODS: Data were from the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE). Participants were 6103 children (mean ± SD age 10.4 ± 0.56 years) from 12 countries who wore an ActiGraph GT3X+ accelerometer on the right hip for approximately one week. We calculated SB time, mean SB bout duration, and SB breaks using a cut-point (SBmovement), CHAP-Child (SBposture), and both methods combined (SBcombined). Mixed effects regression was used to test associations of SB variables with pediatric obesity variables (waist circumference, body fat percentage, and body mass index z-score). RESULTS: After adjusting for MVPA, SBposture showed several significant obesity associations favoring lower mean SB bout duration (b = 0.251-0.449; all p < 0.001) and higher SB breaks (b = -0.005--0.052; all p < 0.001). Lower total SB was unexpectedly related to greater obesity (b = -0.077--0.649; p from <0.001-0.02). For mean SB bout duration and SB breaks, more associations were observed for SBposture (n = 5) than for SBmovement (n = 3) or SBcombined (n = 1), and tended to have larger magnitude as well. CONCLUSIONS: Using traditional measures of low movement as a surrogate for SB may lead to underestimated or undetected adverse associations between SB and obesity. CHAP-Child allows assessment of sitting posture using hip-worn accelerometers. Ongoing work is needed to understand how low movement and posture are related to one another, as well as their potential health implications.

Health effects and cost-effectiveness of a multilevel physical activity intervention in low-income older adults; results from the PEP4PA cluster randomized controlled trial.

BACKGROUND: Older adults are the least active population in the U.S. Low-income communities have fewer physical activity (PA) resources, contributing to less PA and increased chronic disease risk. This study assessed the effect of the multilevel, peer-led, Peer Empowerment Program 4 Physical Activity (PEP4PA) on moderate-to-vigorous PA (MVPA) and health outcomes, over 2 years of follow up. METHODS: In a cluster-randomized controlled trial, 12 senior or community centers serving low-income older adults were assigned to a PA intervention (n = 6) or usual programming (n = 6) condition. PEP4PA included self-monitoring, health coaching, group walks, social support, and community advocacy to improve walking conditions. The primary outcome was daily minutes of MVPA (7-day accelerometer). Secondary outcomes included Perceived Quality of Life (PQoL), 6-Minute Walk Test (6-MWT), blood pressure (BP), and depressive symptoms at baseline, 6, 12, 18 and 24 months. Mixed effects regression models estimated the effects on outcomes between groups over time and included random effects for repeated measures and center clustering. Effect modification by sex and income status was assessed. We calculated the incremental cost per daily minute of MVPA gained in the intervention group relative to the control group to assess cost effectiveness. RESULTS: We enrolled 476 older adults (50 + years). Participants were on average 71 years old, 76% female, 60% low income, and 38% identified as racial or ethnic minorities. Compared to the control group, intervention participants sustained roughly a 10 min/day increase in MVPA from baseline at all time points and increased mean PQoL scores from unsatisfied at baseline to satisfied at 12, 18 and 24 months. Males and higher-income groups had greater improvements in MVPA. No significant effects were observed for 6-MWT or depressive symptoms, and BP results were mixed. The incremental cost per minute MVPA gained per person was $0.25, $0.09, $0.06, and $0.05 at 6, 12, 18 and 24 months, respectively. CONCLUSIONS: PEP4PA achieved increases in MVPA and PQoL in low-income older adults, over 2 years of follow up. The peer-led, community-based intervention provides a sustainable and cost-effective model to improve health behaviors in underserved, aging populations. TRIAL REGISTRATION: ClinicalTrials.gov ( NCT02405325 ) March 20, 2015.

CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children.

BACKGROUND: Hip-worn accelerometer cut-points have poor validity for assessing children's sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. METHODS: Participants were 278, 8-11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). RESULTS: Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. CONCLUSIONS: There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.

Neighborhood built environments and Hispanic/Latino adults' physical activity in the U.S.: The Hispanic community health study/study of Latinos community and surrounding areas study.

Despite experiencing health inequities, less is known about neighborhood environments and physical activity among Hispanic/Latino adults compared to other populations. We investigated this topic in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Hispanic/Latino adults in the San Diego, California area of the U.S. completed measures of overall moderate-to-vigorous physical activity (MVPA) via accelerometry and domain-specific MVPA via questionnaire at Visits 1 (2008-2011; n = 4086) and 2 (2014-2017; n = 1776), ~6 years apart. 800-m home neighborhood buffers were used to create objective measures of residential, intersection, and retail density, bus/trolley stops, greenness, parks, and recreation area at Visit 1. Regression models tested the association of each neighborhood feature with MVPA at Visit 1 and over 6 years, adjusting for individual characteristics and neighborhood socioeconomic deprivation. At Visit 1, those in neighborhoods with higher vs. lower retail density or recreation area (+1 vs. -1 standard deviation from the mean) engaged in 10% more overall MVPA and 12-22% more active transportation. Those in neighborhoods with higher vs. lower residential density engaged in 22% more active transportation. Those in neighborhoods with higher vs. lower greenness and park count engaged in 14-16% more recreational MVPA. Neighborhood features were unassociated with changes in MVPA over 6 years. Although changes in MVPA over time were similar across neighborhoods, Hispanic/Latino adults living in neighborhoods with design features supportive of walking and recreational activity (e.g., greater residential and retail density, more parks and recreation facilities) were consistently more active. Improving neighborhood environments appears important for supporting physical activity among Hispanic/Latino adults.

Air pollution and metabolic disorders: Dynamic versus static measures of exposure among Hispanics/Latinos and non-Hispanics.

INTRODUCTION: Exposure to air pollution disproportionately affects racial/ethnic minorities that could contribute to health inequalities including metabolic disorders. However, most existing studies used a static assessment of air pollution exposure (mostly using the residential address) and do not account for activity space when modelling exposure to air pollution. The aim of this study is to understand how exposure to air pollution impacts metabolic disorders biomarkers, how this effect differs according to ethnicity, and for the first time compare these findings with two methods of exposure assessment: dynamic and static measures. METHODS: Among the Community of Mine study, a cross-sectional study conducted in San Diego County, insulin resistance, diabetes, hypertension, obesity, dyslipidemia, and metabolic syndrome (MetS) were assessed. Exposure to air pollution (PM2.5, NO2, traffic) was calculated using static measures around the home, and dynamic measures of mobility derived from Global Positioning Systems (GPS) traces using kernel density estimators to account for exposure variability across space and time. Associations of air pollution with metabolic disorders were quantified using generalized estimating equation models to account for the clustered nature of the data. RESULTS: Among 552 participants (mean age 58.7 years, 42% Hispanic/Latino), Hispanics/Latinos had a higher exposure to PM2.5 compared to non-Hispanics using static measures. In contrast, Hispanics/Latinos had less exposure to PM2.5 using dynamic measures. For all participants, higher dynamic exposure to PM2.5 and NO2 was associated with increased insulin resistance and cholesterol levels, and increased risk of obesity, dyslipidemia and MetS (RR 1.17, 95% CI: 1.07-1.28; RR 1.21, 95% CI: 1.12-1.30, respectively). The association between dynamic PM2.5 exposure and MetS differed by Hispanic/Latino ethnicity. CONCLUSION: These results highlight the importance of considering people's daily mobility in assessing the impact of air pollution on health.

Neighborhood Socioeconomic Deprivation and Depression Symptoms in Adults From the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

Socioeconomic factors appear to impact mental health conditions such as depression, but little is known about the relative and combined role of neighborhood and personal socioeconomic deprivation among Hispanics/Latinos. This study examined cross-sectional associations of neighborhood and personal socioeconomic deprivation with depression symptoms in a US Hispanic/Latino population from the San Diego Field Center of the Hispanic Community Health Study/Study of Latinos (n = 3,851). Depression symptoms were assessed with the ten-item Centers for Epidemiological Studies in Depression Scale. Neighborhood socioeconomic deprivation was a composite of eleven variables (e.g., neighborhood income, education, employment, household crowding). Greater personal socioeconomic deprivation based on education, income, and employment was generally associated with higher depression symptoms, including after adjusting for neighborhood socioeconomic deprivation. Greater neighborhood socioeconomic deprivation was associated with higher depression symptoms in females but not males, but the association in females became non-significant when adjusting for personal socioeconomic deprivation. Neighborhood socioeconomic deprivation did not significantly interact with personal socioeconomic deprivation in relation to depression symptoms. The present findings support the association of personal socioeconomic status with mental health (indicated by depression symptoms) among Hispanic/Latino populations, whereas neighborhood socioeconomic deprivation did not relate to depression beyond the impact of personal indicators.

Cohort study of respiratory hospital admissions, air quality and sociodemographic factors in preterm infants born in California.

BACKGROUND: Preterm infants suffer from respiratory morbidity especially during the first year of life. OBJECTIVE: To investigate the association of air quality and sociodemographic indicators on hospital admission rates for respiratory causes. METHODS: This is a retrospective cohort study. We identified all live-born preterm infants in California from 2007 to 2012 in a population-based administrative data set and linked them to a data set measuring several air quality and sociodemographic indicators at the census tract level. All sociodemographic and air quality predictors were divided into quartiles (first quartile most favourable to the fourth quartile least favourable). Mixed effect logistic models to account for clustering at the census tract level were used to investigate associations between chronic air quality and sociodemographic indicators respiratory hospital admission during the first year of life. RESULTS: Of 205 178 preterm infants, 5.9% (n = 12 033) were admitted to the hospital for respiratory causes during the first year. In the univariate analysis, comparing the first to the fourth quartile of chronic ozone (risk ratio [RR] 1.29, 95% confidence interval [CI] 1.21, 1.37), diesel (RR 1.10, 95% CI 1.02, 1.17) and particulate matter 2.5 (RR 1.07, 95% CI 1.01, 1.14) exposure were associated with hospital admission during the first year. Following adjustment for confounders, the risk ratios for hospital admission during the first year were 1.53 (95% CI 1.37, 1.72) in relation to educational attainment (per cent of the population over age 25 with less than a high school education) and 1.23 (95% CI 1.09, 1.38) for poverty (per cent of the population living below two times the federal poverty level). CONCLUSIONS: Among preterm infants, respiratory hospital admissions in the first year in California are associated with socioeconomic characteristics of the neighbourhood an individual is living in.

Protocol for a cross sectional study of cancer risk, environmental exposures and lifestyle behaviors in a diverse community sample: the Community of Mine study.

BACKGROUND: Physical inactivity and unhealthy diet are modifiable behaviors that lead to several cancers. Biologically, these behaviors are linked to cancer through obesity-related insulin resistance, inflammation, and oxidative stress. Individual strategies to change physical activity and diet are often short lived with limited effects. Interventions are expected to be more successful when guided by multi-level frameworks that include environmental components for supporting lifestyle changes. Understanding the role of environment in the pathways between behavior and cancer can help identify what environmental conditions are needed for individual behavioral change approaches to be successful, and better recognize how environments may be fueling underlying racial and ethnic cancer disparities. METHODS: This cross-sectional study was designed to select participants (n = 602 adults, 40% Hispanic, in San Diego County) from a range of neighborhoods ensuring environmental variability in walkability and food access. Biomarkers measuring cancer risk were measured with fasting blood draw including insulin resistance (fasting plasma insulin and glucose levels), systemic inflammation (levels of CRP), and oxidative stress measured from urine samples. Objective physical activity, sedentary behavior, and sleep were measured by participants wearing a GT3X+ ActiGraph on the hip and wrist. Objective measures of locations were obtained through participants wearing a Qstarz Global Positioning System (GPS) device on the waist. Dietary measures were based on a 24-h food recall collected on two days (weekday and weekend). Environmental exposure will be calculated using static measures around the home and work, and dynamic measures of mobility derived from GPS traces. Associations of environment with physical activity, obesity, diet, and biomarkers will be measured using generalized estimating equation models. DISCUSSION: Our study is the largest study of objectively measured physical activity, dietary behaviors, environmental context/exposure, and cancer-related biomarkers in a Hispanic population. It is the first to perform high quality measures of physical activity, sedentary behavior, sleep, diet and locations in which these behaviors occur in relation to cancer-associated biomarkers including insulin resistance, inflammation, impaired lipid metabolism, and oxidative stress. Results will add to the evidence-base of how behaviors and the built environment interact to influence biomarkers that increase cancer risk. TRIAL REGISTRATION: ClinicalTrials.gov NCT02094170 , 03/21/2014.

An Online Geographic Data Visualization Tool to Relate Preterm Births to Environmental Factors.

Preterm birth (<37 weeks gestation) continues to be a significant cause of disease and death in the United States. Its complex causes are associated with several genetic, biological, environmental, and sociodemographic factors. Organizing and visualizing various data that may be related to preterm birth is an essential step for pattern exploration and hypothesis generation and presents an opportunity to increase public and stakeholder involvement. In this article, we describe a collaborative effort to create an online geographic data visualization tool using open software to explore preterm birth in Fresno County, where rates are the highest in California. The tool incorporates information on births, environmental exposures, sociodemographic characteristics, the built environment, and access to care. We describe data sets used to build the tool, the data-hosting platform, and the process used to engage stakeholders in its creation. We highlight an important example of how collaboration can increase the utility of geographic data visualization to improve public health and address health equity in birth outcomes.

Environmental pollution and social factors as contributors to preterm birth in Fresno County.

BACKGROUND: Environmental pollution exposure during pregnancy has been identified as a risk factor for preterm birth. Most studies have evaluated exposures individually and in limited study populations. METHODS: We examined the associations between several environmental exposures, both individually and cumulatively, and risk of preterm birth in Fresno County, California. We also evaluated early (< 34 weeks) and spontaneous preterm birth. We used the Communities Environmental Health Screening Tool and linked hospital discharge records by census tract from 2009 to 2012. The environmental factors included air pollution, drinking water contaminants, pesticides, hazardous waste, traffic exposure and others. Social factors, including area-level socioeconomic status (SES) and race/ethnicity were also evaluated as potential modifiers of the relationship between pollution and preterm birth. RESULTS: In our study of 53,843 births, risk of preterm birth was associated with higher exposure to cumulative pollution scores and drinking water contaminants. Risk of preterm birth was twice as likely for those exposed to high versus low levels of pollution. An exposure-response relationship was observed across the quintiles of the pollution burden score. The associations were stronger among early preterm births in areas of low SES. CONCLUSIONS: In Fresno County, we found multiple pollution exposures associated with increased risk for preterm birth, with higher associations among the most disadvantaged. This supports other evidence finding environmental exposures are important risk factors for preterm birth, and furthermore the burden is higher in areas of low SES. This data supports efforts to reduce the environmental burden on pregnant women.

Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

A framework for using GPS data in physical activity and sedentary behavior studies.

Global Positioning Systems (GPS) are applied increasingly in activity studies, yet significant theoretical and methodological challenges remain. This article presents a framework for integrating GPS data with other technologies to create dynamic representations of behaviors in context. Using more accurate and sensitive measures to link behavior and environmental exposures allows for new research questions and methods to be developed.

Innate immunity gene expression changes in critically ill patients with sepsis and disease-related malnutrition.

The aim of this study was an attempt to determine whether the expression of genes involved in innate antibacterial response (TL R2, NOD 1, TRAF6, HMGB 1 and Hsp70) in peripheral blood leukocytes in critically ill patients, may undergo significant changes depending on the severity of the infection and the degree of malnutrition. The study was performed in a group of 128 patients with infections treated in the intensive care and surgical ward. In 103/80.5% of patients, infections had a severe course (sepsis, severe sepsis, septic shock, mechanical ventilation of the lungs). Clinical monitoring included diagnosis of severe infection (according to the criteria of the ACC P/SCC M), assessment of severity of the patient condition and risk of death (APACHE II and SAPS II), nutritional assessment (NRS 2002 and SGA scales) and the observation of the early results of treatment. Gene expression at the mRNA level was analyzed by real-time PCR. The results of the present study indicate that in critically ill patients treated in the IC U there are significant disturbances in the expression of genes associated with innate antimicrobial immunity, which may have a significant impact on the clinical outcome. The expression of these genes varies depending on the severity of the patient condition, severity of infection and nutritional status. Expression disorders of genes belonging to innate antimicrobial immunity should be diagnosed as early as possible, monitored during the treatment and taken into account during early therapeutic treatment (including early nutrition to support the functions of immune cells).

Assessment of the incidence and risk factors of postoperative urosepsis in patients undergoing ureteroscopic lithotripsy.

Introduction: Ureteroscopic lithotripsy (URSL) is an approved, minimally invasive, low-risk procedure for urolithiasis treatment. However, some patients may develop urinary tract infection (UTI) post-procedure, eventually leading to urosepsis. Determining the predictors of infection after URSL would help identify patients at a high risk of urosepsis, thereby enabling the early implementation of effective treatment. Therefore, we aimed to establish the incidence and predictors of urosepsis after URSL. Material and methods: We assessed 231 patients who underwent URSL using a holmium laser. The incidence of urosepsis during the 30-day post-treatment period was analysed, and potential predictors of urosepsis, including patient characteristics and individual clinical factors, were examined. Results: Statistical analysis revealed that 16.88% of patients had a confirmed positive urine culture before the procedure. Post-procedure urosepsis occurred in 4.76% of patients. Univariable analysis revealed that 3 factors were significantly associated with the risk of postoperative urosepsis: double-J stent insertion before URSL, pre-operative positive urine culture, and MDR pathogen found preoperatively. In multivariable analysis, only positive urine culture remained significantly associated with the risk of urosepsis after URSL. Conclusions: Patients with positive urine culture before URSL are at significantly higher risk of urosepsis in the postoperative period. Hence, urine culture should be routinely performed before planned endoscopic urolithiasis treatment.

Assessment of Skin Autofluorescence and Its Association with Glycated Hemoglobin, Cardiovascular Risk Markers, and Concomitant Chronic Diseases in Children with Type 1 Diabetes.

Skin autofluorescence (sAF) measurement is a non-invasive method used to assess tissue advanced glycation end product (AGE) accumulation. This study aims to characterize sAF's association with (1) glycated hemoglobin (HbA1c) values, (2) cardiovascular risk markers, and (3) common comorbidities (autoimmune thyroiditis, celiac disease) in children with type 1 diabetes (T1D). MATERIALS AND METHODS: A total of 348 children with T1D aged 3-18 years and 85 age- and gender-matched control subjects were enrolled. sAF was quantified using an AGE Reader (Diagnoptics BV, The Netherlands). The analysis covered HbA1c, blood lipid, and C-reactive protein (CRP) levels, ambulatory blood pressure monitoring records, and body composition parameters. The associations between variables and sAF were assessed using the Mann-Whitney U test and Spearman correlation. RESULTS: We observed significantly higher sAF values in the T1D group compared to the control (1.40 [1.27-1.53] vs. 1.20 [1.07-1.30, AU]; p = 0.004), consistent across all tested age groups. In the T1D group, sAF was positively correlated with current HbA1c, mean of historical HbA1c values, and T1D duration (r values, respectively: 0.27, 0.22, 0.14, all p < 0.01). Percentage of body fat was positively correlated with sAF (r = 0.120; p = 0.044). No significant correlations were found between sAF and lipid fractions, Z-score of BMI, parameters from 24 h ambulatory blood pressure monitoring, or the amount of albumin excreted in urine. sAF was positively correlated with CRP (r = 0.17, p < 0.05). sAF was significantly higher in patients with concomitant celiac disease (1.53 [1.43-1.63] vs. 1.40 [1.27-1.53, AU], p = 0.001). CONCLUSION: Among young T1D patients with relatively brief diabetes duration, sAF effectively mirrors prior glycemic control, as presented by historical average HbA1c. However, associations with conventional CV risk markers are not evident. The higher sAF values in patients with celiac disease warrant further exploration.

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research.

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data vary from study to study, making comparisons and reproducibility challenging. OBJECTIVES: The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies. DESIGN: A systematic review. DATA SOURCES: Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166). ELIGIBILITY CRITERIA: Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary). DATA EXTRACTION AND SYNTHESIS: We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias. RESULTS: We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data. CONCLUSIONS: Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research. PROSPERO REGISTRATION NUMBER: CRD42022322166.

Social and built neighborhood environments and sleep health: The Hispanic Community Health Study/Study of Latinos Community and Surrounding Areas and Sueño Ancillary Studies.

STUDY OBJECTIVES: To test associations between neighborhood social, built, and ambient environment characteristics and multidimensional sleep health in Hispanic/Latino adults. METHODS: Data were from San Diego-based Hispanic/Latino adults mostly of Mexican heritage enrolled in the Hispanic Community Health Study/Study of Latinos (N = 342). Home addresses were geocoded to ascertain neighborhood characteristics of greenness, walkability (density of intersections, retail spaces, and residences), socioeconomic deprivation (e.g. lower income, lower education), social disorder (e.g. vacant buildings, crime), traffic density, and air pollution (PM 2.5) in the Study of Latinos Communities and Surrounding Areas Study. Sleep dimensions of regularity, satisfaction, alertness, timing, efficiency, and duration were measured by self-report or actigraphy approximately 2 years later. Multivariable regression models accounting for study design (stratification and clustering) were used to examine associations of neighborhood variables with individual sleep dimensions and a multidimensional sleep health composite score. RESULTS: Neighborhood characteristics were not significantly associated with the multidimensional sleep health composite, and there were few significant associations with individual sleep dimensions. Greater levels of air pollution (B = 9.03, 95% CI: 1.16, 16.91) were associated with later sleep midpoint, while greater social disorder (B = -6.90, 95% CI: -13.12, -0.67) was associated with earlier sleep midpoint. Lower walkability was associated with more wake after sleep onset (B = -3.58, 95% CI: -7.07, -0.09). CONCLUSIONS: Living in neighborhoods with lower walkability and greater air pollution was associated with worse sleep health, but otherwise findings were largely null. Future research should test these hypotheses in settings with greater variability and investigate mechanisms of these associations.