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Introduction: Growing evidence exists that greenspace exposure can reduce metabolic syndrome risk, a growing public health concern with well-documented inequities across population subgroups. We capitalize on the use of g-computation to simulate the influence of multiple possible interventions on residential greenspace on nine metabolic biomarkers and metabolic syndrome in adults (N = 555) from the 2014-2017 Community of Mine Study living in San Diego County, California. Methods: Normalized difference vegetation index (NDVI) exposure from 2017 was averaged across a 400-m buffer around the participants' residential addresses. Participants' fasting plasma glucose, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglyceride concentrations, systolic and diastolic blood pressure, hemoglobin A1c (%), waist circumference, and metabolic syndrome were assessed as outcomes of interest. Using parametric g-computation, we calculated risk differences for participants being exposed to each decile of the participant NDVI distribution compared to minimum NDVI. Differential health impacts from NDVI exposure by sex, ethnicity, income, and age were examined. Results: We found that a hypothetical increase in NDVI exposure led to a decrease in hemoglobin A1c (%), glucose, and high-density lipoprotein cholesterol concentrations, an increase in fasting total cholesterol, low-density lipoprotein cholesterol, and triglyceride concentrations, and minimal changes to systolic and diastolic blood pressure, waist circumference, and metabolic syndrome. The impact of NDVI changes was greater in women, Hispanic individuals, and those under 65 years old. Conclusions: G-computation helps to simulate the potential health benefits of differential NDVI exposure and identifies which subpopulations can benefit most from targeted interventions aimed at minimizing health disparities.
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BACKGROUND: Sedentary behavior has been identified as a significant risk factor for Metabolic Syndrome (MetS). However, it is unclear if the sedentary pattern measurement approach (posture vs. movement) impacts observed associations or if associations differ for Hispanic/Latino communities, who have higher risk of MetS. METHODS: Participants from the Community of Mine (CoM) study (N = 602) wore hip-based accelerometers for 14 days and completed MetS-associated biomarker assessment (triglycerides, blood pressure, fasting glucose, HDL cholesterol, waist circumference). Sedentary patterns were classified using both cutpoints (movement-based) and the Convolutional Neural Network Hip Accelerometer Posture (CHAP) algorithm (posture-based). We used logistic regression to estimate associations between MetS with sedentary patterns overall and stratified by Hispanic/Latino ethnicity. RESULTS: CHAP and cutpoint sedentary patterns were consistently associated with MetS. When controlling for total sedentary time and moderate to vigorous physical activity, only CHAP-measured median sedentary bout duration (OR = 1.15, CI: 1.04, 1.28) was significant. In stratified analysis, CHAP-measured median bout duration and time spent in sedentary bouts ≥ 30 min were each associated with increased odds of MetS, but the respective associations were stronger for Hispanic/Latino ethnicity (OR = 1.71 and 1.48; CI = 1.28-2.31 and 1.12-1.98) than for non-Hispanic/Latino ethnicity (OR = 1.43 and 1.40; CI = 1.10-1.87 and 1.06-1.87). CONCLUSIONS: The way sedentary patterns are measured can impact the strength and precision of associations with MetS. These differences may be larger in Hispanic/Latino ethnic groups and warrants further research to inform sedentary behavioral interventions in these populations.
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BACKGROUND: Dietary acculturation, or adoption of dominant culture diet by migrant groups, influences human health. We aimed to examine dietary acculturation and its relationships with cardiovascular disease (CVD), gut microbiota, and blood metabolites among US Hispanic and Latino adults. METHODS: In the HCHS/SOL (Hispanic Community Health Study/Study of Latinos), US exposure was defined by years in the United States (50 states and Washington, DC) and US nativity. A dietary acculturation pattern was derived from 14 172 participants with two 24-hour dietary recalls at baseline (2008-2011) using least absolute shrinkage and selection operator regression, with food groups as predictors of US exposure. We evaluated associations of dietary acculturation with incident CVD across ≈7 years of follow-up (n=211/14 172 cases/total) and gut microbiota (n=2349; visit 2, 2014 to 2017). Serum metabolites associated with both dietary acculturation-related gut microbiota (n=694) and incident CVD (n=108/5256 cases/total) were used as proxy measures to assess the association of diet-related gut microbiome with incident CVD. RESULTS: We identified an empirical US-oriented dietary acculturation score that increased with US exposure. Higher dietary acculturation score was associated with higher risk of incident CVD (hazard ratio per SD, 1.33 [95% CI, 1.13-1.57]), adjusted for sociodemographic, lifestyle, and clinical factors. Sixty-nine microbial species (17 enriched from diverse species, 52 depleted mainly from fiber-utilizing Clostridia and Prevotella species) were associated with dietary acculturation, driven by lower intakes of whole grains, beans, and fruits and higher intakes of refined grains. Twenty-five metabolites, involved predominantly in fatty acid and glycerophospholipid metabolism (eg, branched-chain 14:0 dicarboxylic acid** and glycerophosphoethanolamine), were associated with both diet acculturation-related gut microbiota and incident CVD. Proxy association analysis based on these metabolites suggested a positive relationship between diet acculturation-related microbiome and risk of CVD (r=0.70, P<0.001). CONCLUSIONS: Among US Hispanic and Latino adults, greater dietary acculturation was associated with elevated CVD risk, possibly through alterations in gut microbiota and related metabolites. Diet and microbiota-targeted interventions may offer opportunities to mitigate CVD burdens of dietary acculturation.
Subject(s)
Acculturation , Cardiovascular Diseases , Diet , Gastrointestinal Microbiome , Hispanic or Latino , Humans , Male , Female , Cardiovascular Diseases/blood , Cardiovascular Diseases/ethnology , Middle Aged , United States/epidemiology , Adult , Diet/adverse effects , Risk Factors , IncidenceABSTRACT
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.
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BACKGROUND: Organophosphate, pyrethroid, and neonicotinoid insecticides have resulted in adrenal and gonadal hormone disruption in animal and in vitro studies; limited epidemiologic evidence exists in humans. We assessed relationships of urinary insecticide metabolite concentrations with adrenal and gonadal hormones in adolescents living in Ecuadorean agricultural communities. METHODS: In 2016, we examined 522 Ecuadorian adolescents (11-17y, 50.7% female, 22% Indigenous; ESPINA study). We measured urinary insecticide metabolites, blood acetylcholinesterase activity (AChE), and salivary testosterone, dehydroepiandrosterone (DHEA), 17ß-estradiol, and cortisol. We used general linear models to assess linear (ß = % hormone difference per 50% increase of metabolite concentration) and curvilinear relationships (ß2 = hormone difference per unit increase in squared ln-metabolite) between ln-metabolite or AChE and ln-hormone concentrations, stratified by sex, adjusting for anthropometric, demographic, and awakening response variables. Bayesian Kernel Machine Regression was used to assess non-linear associations and interactions. RESULTS: The organophosphate metabolite malathion dicarboxylic acid (MDA) had positive associations with testosterone (ßboys = 5.88% [1.21%, 10.78%], ßgirls = 4.10% [-0.02%, 8.39%]), and cortisol (ßboys = 6.06 [-0.23%, 12.75%]. Para-nitrophenol (organophosphate) had negatively-trending curvilinear associations, with testosterone (ß2boys = -0.17 (-0.33, -0.003), p = 0.04) and DHEA (ß2boys = -0.49 (-0.80, -0.19), p = 0.001) in boys. The neonicotinoid summary score (ßboys = 5.60% [0.14%, 11.36%]) and the neonicotinoid acetamiprid-N-desmethyl (ßboys = 3.90% [1.28%, 6.58%]) were positively associated with 17ß-estradiol, measured in boys only. No associations between the pyrethroid 3-phenoxybenzoic acid and hormones were observed. In girls, bivariate response associations identified interactions of MDA, Para-nitrophenol, and 3,5,6-trichloro-2-pyridinol (organophosphates) with testosterone and DHEA concentrations. In boys, we observed an interaction of MDA and Para-nitrophenol with DHEA. No associations were identified for AChE. CONCLUSIONS: We observed evidence of endocrine disruption for specific organophosphate and neonicotinoid metabolite exposures in adolescents. Urinary organophosphate metabolites were associated with testosterone and DHEA concentrations, with stronger associations in boys than girls. Urinary neonicotinoids were positively associated with 17ß-estradiol. Longitudinal repeat-measures analyses would be beneficial for causal inference.
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Biomarkers , Insecticides , Humans , Adolescent , Female , Male , Ecuador , Insecticides/urine , Insecticides/blood , Biomarkers/urine , Biomarkers/blood , Child , Hydrocortisone/urine , Dehydroepiandrosterone/urine , Dehydroepiandrosterone/blood , Estradiol/blood , Estradiol/urine , Agriculture , Acetylcholinesterase/blood , Acetylcholinesterase/metabolism , Testosterone/blood , Testosterone/urine , Saliva/chemistry , Malathion/urineABSTRACT
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.
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Geographic Information Systems , Humans , Data Collection/methods , Data Collection/standards , Environmental Exposure , Reproducibility of ResultsABSTRACT
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.
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Environment , Food Supply , Humans , Food , CommerceABSTRACT
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 (Nâ =â 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 (Bâ =â 9.03, 95% CI: 1.16, 16.91) were associated with later sleep midpoint, while greater social disorder (Bâ =â -6.90, 95% CI: -13.12, -0.67) was associated with earlier sleep midpoint. Lower walkability was associated with more wake after sleep onset (Bâ =â -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.
Subject(s)
Hispanic or Latino , Neighborhood Characteristics , Sleep Initiation and Maintenance Disorders , Sleep , Humans , Residence Characteristics , Self Report , Social Determinants of HealthABSTRACT
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 â¼460 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.
Subject(s)
Air Pollutants , Air Pollution , Humans , Female , Overweight/epidemiology , Geographic Information Systems , Longitudinal Studies , C-Reactive Protein/analysis , Environmental Exposure/analysis , Obesity , Particulate Matter/analysis , Glucose , Air Pollutants/analysis , Air Pollution/analysisABSTRACT
Study Objectives: Examining multiple dimensions of sleep health may better capture associations between sleep and health risks, including cardiometabolic disease (CMD). Hispanics have elevated risk for inadequate sleep and CMD biomarkers. Few studies have explored whether associations between sleep and CMD differ by Hispanic ethnicity. Methods: Leveraging data from the Community of Mine (CoM) study, a cross-sectional investigation of 602 ethnically diverse participants, we derived accelerometer-measured sleep duration and efficiency, and self-reported sleep quality. Accelerometer-measured sleep exposures were analyzed both as continuous and categorical variables. Multivariate and quantile regression models were used to assess associations between sleep and CMD biomarkers (insulin resistance, systolic blood pressure, and low-density-lipoprotein cholesterol), controlling for age, sex, ethnicity, education, smoking status, and body mass index. We examined the potential effect modification of Hispanic ethnicity. Results: We observed mixed results based on CMD biomarkers and sleep exposure. Increased sleep duration was significantly related to low-density lipoprotein cholesterol in adjusted models (estimateâ =â 0.06; 95% CI: 0.02, 0.11). Poor sleep efficiency was associated with greater insulin resistance in the adjusted quantile (estimateâ =â 0.20; 95% CI: 0.04, 0.36) model at the 90th percentile. Self-reported sleep quality was not associated with CMD outcomes. There was no evidence of effect modification by Hispanic ethnicity. Conclusions: In this cohort, sleep health measures were found to have mixed and at times opposing effects on CMD outcomes. These effects did not demonstrate an interaction with Hispanic ethnicity.
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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.
Subject(s)
Pediatric Obesity , Child , Humans , Pediatric Obesity/epidemiology , Sedentary Behavior , Exercise , Life Style , Body Mass Index , Accelerometry/methodsABSTRACT
Evidence linking traffic noise to insulin resistance and diabetes is limited and unanswered questions remain regarding the potential effect modification by neighborhood socioeconomic status (nSES). We aimed to assess socioeconomic inequalities in noise exposure, whether road and aircraft noise exposures were associated with insulin resistance or diabetes, and whether nSES modified these relationships. Among the Community of Mine Study in San Diego County, road and aircraft noise exposure at enrollment was calculated based on the static (participant's administrative boundary, and circular buffer around participant homes), and dynamic (mobility data by global positioning system, GPS) spatio-temporal aggregation methods. Associations of noise with insulin resistance (HOMA-IR) or type 2 diabetes (T2DM) were quantified using generalized estimating equation models adjusted for sex, age, ethnicity, individual income, and air pollution (nitrogen dioxide) exposure. Additive interaction between noise and nSES was assessed. Among 573 participants (mean age 58.7 y), participants living in low nSES were exposed to higher levels of aircraft and road noise using noise level at the census tract, circular buffer, or Kernel Density Estimation (KDE) of GPS data. Participants exposed to road noise greater or equal to the median (53 dB(A)) at the census tract and living in low nSES had an increased level of insulin resistance (ß = 0.15, 95%CI: -0.04, 0.34) and higher odds of T2DM (Odds Ratio = 2.34, 95%CI: 1.12, 4.90). A positive additive interaction was found as participants living in low nSES had higher odds of T2DM. The impact of noise exposure on insulin resistance and T2DM differs substantially by nSES. Public health benefits of reducing exposure to road or aircraft noise would be larger in individuals living in low nSES.
Subject(s)
Diabetes Mellitus, Type 2 , Insulin Resistance , Noise, Transportation , Humans , Middle Aged , Noise, Transportation/adverse effects , Diabetes Mellitus, Type 2/epidemiology , Social Class , Aircraft , Environmental ExposureABSTRACT
PURPOSE: It remains unclear why individuals living in disadvantaged neighborhoods have shorter non-small cell lung cancer (NSCLC) survival. It is possible that living in these deprived areas is linked with increased risk of developing aggressive NSCLC biology. Here, we explored the association of somatic KRAS mutations, which are associated with shorter survival in NSCLC patients, and 11 definitions of neighborhood disadvantage spanning socioeconomic and structural environmental elements. METHODS: We analyzed data from 429 NSCLC patients treated at a Comprehensive Cancer Center from 2015 to 2018. Data were abstracted from medical records and each patient's home address was used to assign publicly available indices of neighborhood disadvantage. Prevalence Ratios (PRs) for the presence of somatic KRAS mutations were estimated using modified Poisson regression models adjusted for age, sex, smoking status, race/ethnicity, educational attainment, cancer stage, and histology. RESULTS: In the NSCLC cohort, 29% had KRAS mutation-positive tumors. We found that five deprivation indices of socioeconomic disadvantage were associated with KRAS mutation. A one decile increase in several of these socioeconomic disadvantage indices was associated with a 1.06 to 1.14 increased risk of KRAS mutation. Measures of built structural environment were not associated with KRAS mutation status. CONCLUSION: Socioeconomic disadvantage at the neighborhood level is associated with higher risk of KRAS mutation while disadvantage related to built environmental structural measures was inversely associated. Our results indicate not only that neighborhood disadvantage may contribute to aggressive NSCLC biology, but the pathways linking biology to disadvantage are likely operating through socioeconomic-related stress.
Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Humans , Carcinoma, Non-Small-Cell Lung/epidemiology , Carcinoma, Non-Small-Cell Lung/genetics , Proto-Oncogene Proteins p21(ras)/genetics , Lung Neoplasms/epidemiology , Lung Neoplasms/genetics , Residence Characteristics , Neighborhood Characteristics , MutationABSTRACT
Time-weighted spatial averaging approaches (TWSA) are an increasingly utilized method for calculating exposure using global positioning system (GPS) mobility data for health-related research. They can provide a time-weighted measure of exposure, or dose, to various environments or health hazards. However, little work has been done to compare existing methodologies, nor to assess how sensitive these methods are to mobility data inputs (e.g., walking vs driving), the type of environmental data being assessed as the exposure (e.g., continuous surfaces vs points of interest), and underlying point-pattern clustering of participants (e.g., if a person is highly mobile vs predominantly stationary). Here we contrast three TWSA approaches that have been previously used or recently introduced in the literature: Kernel Density Estimation (KDE), Density Ranking (DR), and Point Overlay (PO). We feed GPS and accelerometer data from 602 participants through each method to derive time-weighted activity spaces, comparing four mobility behaviors: all movement, stationary time, walking time, and in-vehicle time. We then calculate exposure values derived from the various TWSA activity spaces with four environmental layer data types (point, line, area, surface). Similarities and differences across TWSA derived exposures for the sample and between individuals are explored, and we discuss interpretation of TWSA outputs providing recommendations for researchers seeking to apply these methods to health-related studies.
Subject(s)
Environmental Exposure , Geographic Information Systems , Humans , Walking , Spatial AnalysisABSTRACT
Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics in which important information of PA temporal/diurnal patterns is lost. In this paper we propose a novel functional data analysis approach based on Riemann manifolds for modeling PA and its longitudinal changes. We model smoothed minute-level PA of a day as one-dimensional Riemann manifolds and longitudinal changes in PA in different visits as deformations between manifolds. The variability in changes of PA among a cohort of subjects is characterized via variability in the deformation. Functional principal component analysis is further adopted to model the deformations, and PC scores are used as a proxy in modeling the relation between changes in PA and health outcomes and/or interventions. We conduct comprehensive analyses on data from two clinical trials: Reach for Health (RfH) and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect of interventions on longitudinal changes in PA patterns and how different modes of changes in PA influence weight loss, respectively. The proposed approach reveals unique modes of changes, including overall enhanced PA, boosted morning PA, and shifts of active hours specific to each study cohort. The results bring new insights into the study of longitudinal changes in PA and health and have the potential to facilitate designing of effective health interventions and guidelines.
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Accumulating evidence links cardiometabolic health with social and environmental neighborhood exposures, which may contribute to health inequities. We examined whether environmental characteristics were individually or jointly associated with insulin resistance, hypertension, obesity, type 2 diabetes, and metabolic syndrome in San Diego County, CA. As part of the Community of Mine Study, cardiometabolic outcomes of insulin resistance, hypertension, BMI, diabetes, and metabolic syndrome were collected in 570 participants. Seven census tract level characteristics of participants' residential environment were assessed and grouped as follows: economic, education, health care access, neighborhood conditions, social environment, transportation, and clean environment. Generalized estimating equation models were performed, to take into account the clustered nature of the data and to estimate ß or relative risk (RR) and 95 % confidence intervals (CIs) between each of the seven environmental characteristics and cardiometabolic outcomes. Quantile g-computation was used to examine the association between the joint effect of a simultaneous increase in all environmental characteristics and cardiometabolic outcomes. Among 570 participants (mean age 58.8 ± 11 years), environmental economic, educational and health characteristics were individually associated with insulin resistance, diabetes, obesity, and metabolic syndrome. In the mixture analyses, a joint quartile increase in all environmental characteristics (i.e., improvement) was associated with decreasing insulin resistance (ß, 95 %CI: -0.09, -0.18-0.01)), risk of diabetes (RR, 95 %CI: 0.59, 0.36-0.98) and obesity (RR, 95 %CI: 0.81, 0.64-1.02). Environmental characteristics synergistically contribute to cardiometabolic health and independent analysis of these determinants may not fully capture the potential health impact of social and environmental determinants of health.
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Limited previous work has identified a relationship between exposure to ambient air pollution and aggressive somatic lung tumor mutations. More work is needed to confirm this relationship, especially using spatially resolved air pollution. We aimed to quantify the association between different air pollution metrics and aggressive tumor biology. Among patients treated at City of Hope Comprehensive Cancer Center in Duarte, CA (2013-2018), three non-small cell lung cancer somatic tumor mutations, TP53, KRAS, and KRAS G12C/V, were documented. PM2.5 exposure was assessed using state-of-the art ensemble models five and ten years before lung cancer diagnosis. We also explored the role of NO2 using inverse-distance-weighting approaches. We fitted logistic regression models to estimate odds ratio (OR) and their 95% confidence intervals (CIs). Among 435 participants (median age: 67, female: 51%), an IQR increase in NO2 exposure (3.5 µg/m3) five years before cancer diagnosis was associated with an increased risk in TP53 mutation (OR, 95% CI: 1.30, 0.99-1.71). We found an association between highly-exposed participants to PM2.5 (>12 µg/m3) five and ten years before cancer diagnosis and TP53 mutation (OR, 95% CI: 1.61, 0.95-2.73; 1.57, 0.93-2.64, respectively). Future studies are needed to confirm this association and better understand how air pollution impacts somatic profiles and the molecular mechanisms through which they operate.
Subject(s)
Air Pollution , Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Particulate Matter , Aged , Air Pollutants/adverse effects , Air Pollutants/analysis , Air Pollution/adverse effects , Air Pollution/analysis , Carcinoma, Non-Small-Cell Lung/epidemiology , Carcinoma, Non-Small-Cell Lung/genetics , Environmental Exposure/adverse effects , Environmental Exposure/analysis , Female , Humans , Los Angeles/epidemiology , Lung Neoplasms/etiology , Lung Neoplasms/genetics , Mutation , Nitrogen Dioxide/adverse effects , Particulate Matter/adverse effects , Particulate Matter/analysis , Proto-Oncogene Proteins p21(ras)ABSTRACT
We examined associations of micro-scale environment attributes (e.g., sidewalks, street crossings) with three physical activity (PA) measures among Hispanic/Latino adults (nâ¯=â¯1776) living in San Diego County, CA. Systematic observation was used to quantify micro-scale environment attributes near each participant's home. Total PA was assessed with accelerometers, and PA for transportation and recreation were assessed by validated self-report. Although several statistically significant interactions between individual and neighborhood characteristics were identified, there was little evidence micro-scale attributes were related to PA. An important limitation was restricted environmental variability for this sample which lived in a small area of a single county.
Subject(s)
Environment Design , Pedestrians , Exercise , Hispanic or Latino , Humans , Residence Characteristics , WalkingABSTRACT
BACKGROUND: Risk of nonrelapse mortality (NRM) after hematopoietic cell transplantation (HCT) is high. Patient-level clinical prediction models such as the HCT-comorbidity index (HCT-CI) help identify those at increased risk for NRM, but the independent contribution of social determinants of health on HCT outcomes is not well characterized. METHODS: This study included 1602 patients who underwent allogeneic HCT between 2013 and 2019 at City of Hope. Census tract-level social vulnerability was measured using the social vulnerability index (SVI). Fine-Gray multivariable regression evaluated the association between SVI and 1-year NRM. Subgroup analysis examined risk of NRM across combined SVI and HCT-CI categories and by race and ethnicity. RESULTS: Cumulative incidence of 1-year NRM after HCT was 15.3% (95% confidence interval [CI] = 13.6% to 17.1%). In multivariable analysis, patients in the highest SVI tertile (highest social vulnerability) had a 1.4-fold risk (subdistribution hazard ratio [sHR] = 1.36, 95% CI = 1.04 to 1.78) of NRM compared with individuals in the lower tertiles; patients in the highest SVI tertile who also had elevated (≥3) HCT-CI scores had the highest risk (sHR = 1.81, 95% CI = 1.26 to 2.58) of 1-year NRM (reference: lower SVI tertiles and HCT-CI < 3). High social vulnerability was associated with risk of 1-year NRM in Asian (sHR = 2.03, 95% CI = 1.09 to 3.78) and Hispanic (sHR = 1.63, 95% CI = 1.04 to 2.55) but not non-Hispanic White patients. CONCLUSIONS: High social vulnerability independently associated with 1-year NRM after HCT, specifically among minority populations and those with a high comorbidity burden at HCT. These findings may inform targeted approaches for needs assessment during and after HCT, allowing for timely interventions to improve health outcomes in at-risk patients.
Subject(s)
Hematopoietic Stem Cell Transplantation , Social Vulnerability , Humans , Transplantation, Homologous , Hematopoietic Stem Cell Transplantation/adverse effects , Proportional Hazards Models , Comorbidity , Retrospective StudiesABSTRACT
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.