Prospective transitions in hemoglobin A1c following gestational diabetes using Multistate Markov Models.

OBJECTIVE: We characterized the state-to-state transitions in postpartum A1c levels after gestational diabetes, including remaining in a state of normoglycemia or transitions between prediabetes or diabetes states of varying severity. METHODS: We used data from the APPLE Cohort, a postpartum population-based cohort of individuals with gestational diabetes between 2009-2011and linked HbA1c data with up to 9 years follow-up (N=34,171). We examined maternal sociodemographic and perinatal characteristics as predictors of transitions in A1c progression using Markov multistate models. RESULTS: In the first-year postpartum following gestational diabetes, 45.1% of people had no-diabetes, 43.1% had prediabetes, 4.6% had controlled diabetes and 7.2% had uncontrolled diabetes. Roughly two-thirds of individuals remained in same state in the next year. Black individuals were more likely to transition from pre-diabetes to uncontrolled diabetes (aHR: 2.32 95% CI: 1.21 ,4.47) than White persons. Perinatal risk factors were associated with disease progression and lower likelihood of improvement. For example, hypertensive disorders of pregnancy were associated with a stronger transition (aHR: 2.06 95% CI: 1.39, 3.05) from prediabetes to uncontrolled diabetes. CONCLUSIONS: We illustrate factors associated with adverse transitions in incremental A1c stages and describe patient profiles who may warrant enhanced postpartum monitoring.

Estimating effects of longitudinal and cumulative exposure to PFAS mixtures on early adolescent body composition.

Few methods have been used to characterize repeatedly measured biomarkers of chemical mixtures. We applied latent profile analysis (LPA) to serum concentrations of 4 perfluoroalkyl and polyfluoroalkyl substances (PFAS) measured at 4 time points from gestation to age 12 years. We evaluated the relationships between profiles and z scores of height, body mass index, fat mass index, and lean body mass index at age 12 years (n = 218). We compared LPA findings with an alternative approach for cumulative PFAS mixtures using g-computation to estimate the effect of simultaneously increasing the area under the receiver operating characteristic curve (AUC) for all PFAS. We identified 2 profiles: a higher PFAS profile (35% of sample) and a lower PFAS profile (relative to each other), based on their average PFAS concentrations at all time points. The higher PFAS profile had generally lower z scores for all outcomes, with somewhat larger effects for males, though all 95% CIs crossed the null. For example, the higher PFAS profile was associated with a 0.50-unit lower (ß = -0.50; 95% CI, -1.07 to 0.08) BMI z score among males but not among females (ß = 0.04; 95% CI, -0.45 to 0.54). We observed similar patterns with AUCs. We found that a higher childhood PFAS profile and higher cumulative PFAS mixtures may be associated with altered growth in early adolescence. This article is part of a Special Collection on Environmental Epidemiology.

Exposure to per- and polyfluoroalkyl substances and alterations in plasma microRNA profiles in children.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals that persist in the environment and can accumulate in humans, leading to adverse health effects. MicroRNAs (miRNAs) are emerging biomarkers that can advance the understanding of the mechanisms of PFAS effects on human health. However, little is known about the associations between PFAS exposures and miRNA alterations in humans. OBJECTIVE: To investigate associations between PFAS concentrations and miRNA levels in children. METHODS: Data from two distinct cohorts were utilized: 176 participants (average age 17.1 years; 75.6% female) from the Teen-Longitudinal Assessment of Bariatric Surgery (Teen-LABS) cohort in the United States, and 64 participants (average age 6.5 years, 39.1% female) from the Rhea study, a mother-child cohort in Greece. PFAS concentrations and miRNA levels were assessed in plasma samples from both studies. Associations between individual PFAS and plasma miRNA levels were examined after adjusting for covariates. Additionally, the cumulative effects of PFAS mixtures were evaluated using an exposure burden score. Ingenuity Pathways Analysis was employed to identify potential disease functions of PFAS-associated miRNAs. RESULTS: Plasma PFAS concentrations were associated with alterations in 475 miRNAs in the Teen-LABs study and 5 miRNAs in the Rhea study (FDR p < 0.1). Specifically, plasma PFAS concentrations were consistently associated with decreased levels of miR-148b-3p and miR-29a-3p in both cohorts. Pathway analysis indicated that PFAS-related miRNAs were linked to numerous chronic disease pathways, including cardiovascular diseases, inflammatory conditions, and carcinogenesis. CONCLUSION: Through miRNA screenings in two independent cohorts, this study identified both known and novel miRNAs associated with PFAS exposure in children. Pathway analysis revealed the involvement of these miRNAs in several cancer and inflammation-related pathways. Further studies are warranted to enhance our understanding of the relationships between PFAS exposure and disease risks, with miRNA emerging as potential biomarkers and/or mediators in these complex pathways.

Associations between neighborhood characteristics and child well-being before and during the COVID-19 pandemic: A repeated cross-sectional study in the Environmental influences on Child Health Outcomes (ECHO) program.

The corona virus disease (COVID-19) pandemic disrupted daily life worldwide, and its impact on child well-being remains a major concern. Neighborhood characteristics affect child well-being, but how these associations were affected by the pandemic is not well understood. We analyzed data from 1039 children enrolled in the Environmental influences on Child Health Outcomes Program whose well-being was assessed using the Patient-Reported Outcomes Measurement Information System Global Health questionnaire and linked these data to American Community Survey (ACS) data to evaluate the impacts of neighborhood characteristics on child well-being before and during the pandemic. We estimated the associations between more than 400 ACS variables and child well-being t-scores stratified by race/ethnicity (non-Hispanic white vs. all other races and ethnicities) and the timing of outcome data assessment (pre-vs. during the pandemic). Network graphs were used to visualize the associations between ACS variables and child well-being t-scores. The number of ACS variables associated with well-being t-scores decreased during the pandemic period. Comparing non-Hispanic white with other racial/ethnic groups during the pandemic, different ACS variables were associated with child well-being. Multiple ACS variables representing census tract-level housing conditions and neighborhood racial composition were associated with lower well-being t-scores among non-Hispanic white children during the pandemic, while higher percentage of Hispanic residents and higher percentage of adults working as essential workers in census tracts were associated with lower well-being t-scores among non-white children during the same study period. Our study provides insights into the associations between neighborhood characteristics and child well-being, and how the COVID-19 pandemic affected this relationship.

Pre-natal and early life lead exposure and childhood inhibitory control: an item response theory approach to improve measurement precision of inhibitory control.

BACKGROUND: Neurodevelopmental performance tasks are often separately analyzed, even when they tap into a similar construct. This may yield mixed findings for associations of an exposure-neurobehavioral outcome. We develop an item response theory (IRT) approach to integrate multiple task variables together to improve measurement precision of the underlying construct. We apply this approach to create an integrative measure of childhood inhibitory control, and study impacts of pre/post-natal lead exposure. METHODS: Using data from a prospective cohort based in Mexico (N = 533), we created an inhibitory control scale that integrates accuracy and reaction time information from four inhibitory control tasks (Go/NoGo Letter, Go/NoGo Neutral, Go/NoGo Happy, Delis-Kaplan Executive Function System (D-KEFS) Color-Word Interference Test, Condition 3). Using a generalized partial credit item response theory model, we estimated an inhibitory control index for each participant. We then assessed adjusted associations between umbilical cord blood and 4-year lead and childhood inhibitory control. We developed a resampling approach to incorporate error estimates from the inhibitory control variable to confirm the consistency of the lead-inhibitory control associations. We modeled time-varying associations of lead with each inhibitory control measure separately. RESULTS: Participants had a median age of 9 years; 51.4% were males. Umbilical cord blood [-0.06 (95% CI: -0.11, -0.01)] and 4-year lead [-0.07 (95% CI: -0.12, -0.02)] were associated with inhibitory control index at 8-10 years. A resampling approach confirmed that 4-year lead was consistently associated with childhood inhibitory control index. Umbilical cord blood and 4-year lead were each associated with 3 out of 8 measures in separate models. CONCLUSION: This is the first application of IRT in environmental epidemiology to create a latent variable for inhibitory control that integrates accuracy and reaction time information from multiple, related tasks. This framework can be applied to other correlated neurobehavioral assessments or other phenotype data.

Toward Advancing Precision Environmental Health: Developing a Customized Exposure Burden Score to PFAS Mixtures to Enable Equitable Comparisons Across Population Subgroups, Using Mixture Item Response Theory.

Quantifying a person's cumulative exposure burden to per- and polyfluoroalkyl substances (PFAS) mixtures is important for risk assessment, biomonitoring, and reporting of results to participants. However, different people may be exposed to different sets of PFASs due to heterogeneity in the exposure sources and patterns. Applying a single measurement model for the entire population (e.g., by summing concentrations of all PFAS analytes) assumes that each PFAS analyte is equally informative to PFAS exposure burden for all individuals. This assumption may not hold if PFAS exposure sources systematically differ within the population. However, the sociodemographic, dietary, and behavioral characteristics that underlie systematic exposure differences may not be known, or may be due to a combination of these factors. Therefore, we used mixture item response theory, an unsupervised psychometrics and data science method, to develop a customized PFAS exposure burden scoring algorithm. This scoring algorithm ensures that PFAS burden scores can be equitably compared across population subgroups. We applied our methods to PFAS biomonitoring data from the United States National Health and Nutrition Examination Survey (2013-2018). Using mixture item response theory, we found that participants with higher household incomes had higher PFAS burden scores. Asian Americans had significantly higher PFAS burden compared with non-Hispanic Whites and other race/ethnicity groups. However, some disparities were masked when using summed PFAS concentrations as the exposure metric. This work demonstrates that our summary PFAS burden metric, accounting for sources of exposure variation, may be a more fair and informative estimate of PFAS exposure.

Predictors and Trends in First-Trimester Hemoglobin A1c Screening in New York City, 2009 to 2017.

Glycated hemoglobin is an adjunct tool in early pregnancy to assess glycemic control. We examined trends and maternal predictors for those who had A1c screening in early pregnancy using hospital discharge and vital registry data between 2009 and 2017 linked with the New York City A1C Registry (N = 798,312). First-trimester A1c screening increased from 2.3% in 2009 to 7.7% in 2017. The likelihood of screening became less targeted to high-risk patients over time, with a decrease in mean A1c values from 5.8% (95% confidence interval [CI]: 5.8, 5.9) to 5.3 (95% CI: 5.3, 5.4). The prevalence of gestational diabetes mellitus increased while testing became less discriminate for those with high-risk factors, including pregestational type 2 diabetes, chronic hypertension, obesity, age over 40 years, as well as Asian or Black non-Hispanic race/ethnicity. KEY POINTS: · First-trimester A1c screening increased from 2.3% in 2009 to 7.7% in 2017 in New York City.. · The likelihood of screening became less targeted to high-risk patients over time.. · The prevalence of gestational diabetes mellitus increased, while testing became less discriminate..

Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients.

There is growing concern that the social and physical distancing measures implemented in response to the Covid-19 pandemic may negatively impact health in other areas, via both decreased physical activity and increased social isolation. Here, we investigated whether increased engagement with digital social tools may help mitigate effects of enforced isolation on physical activity and mood, in a naturalistic study of at-risk individuals. Passively sensed smartphone app use and actigraphy data were collected from a group of psychiatric outpatients before and during imposition of strict Covid-19 lockdown measures. Data were analysed using Gaussian graphical models: a form of network analysis which gives insight into the predictive relationships between measures across timepoints. Within-individuals, we found evidence of a positive predictive path between digital social engagement, general smartphone use, and physical activity-selectively under lockdown conditions (N = 127 individual users, M = 6201 daily observations). Further, we observed a positive relationship between social media use and total daily steps across individuals during (but not prior to) lockdown. Although there are important limitations on the validity of drawing causal conclusions from observational data, a plausible explanation for our findings is that, during lockdown, individuals use their smartphones to access social support, which may help guard against negative effects of in-person social deprivation and other pandemic-related stress. Importantly, passive monitoring of smartphone app usage is low burden and non-intrusive. Given appropriate consent, this could help identify people who are failing to engage in usual patterns of digital social interaction, providing a route to early intervention.

Prenatal exposure to PM2.5 and childhood cognition: Accounting for between-site heterogeneity in a pooled analysis of ECHO cohorts in the Northeastern United States.

BACKGROUND: Emerging studies have investigated the adverse health effects of PM2.5 using data from multiple cohorts, and results often are not generalizable across cohorts. We aimed to assess associations between prenatal PM2.5 and childhood cognition in two U.S. cohorts while accounting for between-site heterogeneity. METHODS: Analyses included 348 mother-child dyads enrolled in the dual site (New York City and Boston) PRogramming of Intergenerational Stress Mechanisms (PRISM) cohort and in the First Thousand Days of Life (FTDL) study (Northern Virginia) participating in the Environmental influences on Child Health Outcomes (ECHO) national consortium. Residential prenatal PM2.5 exposure was estimated using a validated satellite-based model and childhood cognition was measured using the NIH Toolbox Cognition Battery at three to eight years of age. We used a log-linear model applied to contingency tables formed by cross-classifying covariates by site to examine between-site heterogeneity using 3rd trimester PM2.5 exposure, age-corrected cognition scores, and covariates potentially causing heterogeneities. Multivariable linear regression models informed by the combinability analysis were used to estimate the coefficients and 95% confidence intervals (CIs) for the association between 3rd trimester PM2.5 exposure and age-corrected cognition scores (mean = 100, SD = 15). RESULTS: The log-linear model indicated that inter-study associations were similar between PRISM-NYC and FTDL, which were different from those in PRISM-Boston. Accordingly, we combined the data of PRISM-NYC and FTDL cohorts. We observed associations between 3rd trimester PM2.5 and cognition scores, findings were varying by site, childsex, and test. For example, a 1 µg/m3 increase of 3rd trimester PM2.5 was associated with -4.35 (95% CI = -8.73, -0.25) mean early childhood cognition scores in females in PRISM-Boston. In the pooled NYC + FTDL site, the association between PM2.5 and childhood cognition may be modified by maternal education and urbanicity. CONCLUSIONS: We found associations between prenatal PM2.5 and impaired childhood cognition. Since multi-site analyses are increasingly conducted, our findings suggest the needed awareness of between-site heterogeneity.

Evaluating the Impact of New York's Executive Order on Face Mask Use on COVID-19 Cases and Mortality: a Comparative Interrupted Times Series Study.

BACKGROUND: On April 17, 2020, the State of New York (NY) implemented an Executive Order that requires all people in NY to wear a face mask or covering in public settings where social distancing cannot be maintained. Although the Centers for Disease Control and Prevention recommended face mask use by the general public, there is a lack of evidence on the effect of face mask policies on the spread of COVID-19 at the state level. OBJECTIVE: To assess the impact of the Executive Order on face mask use on COVID-19 cases and mortality in NY. DESIGN: A comparative interrupted time series analysis was used to assess the impact of the Executive Order in NY with Massachusetts (MA) as a comparison state. PARTICIPANTS: We analyzed data on COVID-19 in NY and MA from March 25 to May 6, 2020. INTERVENTION: The Executive Order on face mask use in NY. MAIN MEASURES: Daily numbers of COVID-19 confirmed cases and deaths. KEY RESULTS: The average daily number of confirmed cases in NY decreased from 8549 to 5085 after the Executive Order took effect, with a trend change of 341 (95% CI, 187-496) cases per day. The average daily number of deaths decreased from 521 to 384 during the same two time periods, with a trend change of 52 (95% CI, 44-60) deaths per day. Compared to MA, the decreasing trend in NY was significantly greater for both daily numbers of confirmed cases (P = 0.003) and deaths (P < 0.001). CONCLUSIONS: The Executive Order on face mask use in NY led to a significant decrease in both daily numbers of COVID-19 confirmed cases and deaths. Findings from this study provide important evidence to support state-level policies that require face mask use by the general public.

Achieving Value in Population Health Big Data.

Several population health big data projects have been initiated in the USA recently. These include the County Health Rankings & Roadmaps (CHR) initiated in 2010, the 500 Cities Project initiated in 2016, and the City Health Dashboard project initiated in 2017. Such projects provide data on a range of factors that determine health-such as socioeconomic factors, behavioral factors, health care access, and environmental factors-either at the county or city level. They provided state-of-the-art data visualization and interaction tools so that clinicians, public health practitioners, and policymakers can easily understand population health data at the local level. However, these recent initiatives were all built from data collected using long-standing and extant public health surveillance systems from organizations such as the Centers for Disease Control and Prevention and the U.S. Census Bureau. This resulted in a large extent of similarity among different datasets and a potential waste of resources. This perspective article aims to elaborate on the diminishing returns of creating more population health datasets and propose potential ways to integrate with clinical care and research, driving insights bidirectionally, and utilizing advanced analytical tools to improve value in population health big data.

Clinical validation of the Neonatal Infant Stressor Scale with preterm infant salivary cortisol.

BACKGROUND: Preterm infants face unique stress states in early life. Early-life stress has been associated with changes in cortisol reactivity and behavioral abnormalities later in childhood in non-preterm populations. The Neonatal Infant Stressor Scale (NISS) has been used to estimate infant stress in the neonatal intensive care unit (NICU) but has not been biomarker validated. The relationship between NISS scores and salivary cortisol is unknown. The aim of this study is to test the association between NISS scores and salivary cortisol in the NICU Hospital Exposures and Long-Term Health (NICU-HEALTH) preterm birth cohort. METHODS: Three hundred and eighty-six salivary cortisol specimens were collected from 125 NICU-HEALTH participants during the NICU hospitalization. NISS scores were calculated to represent the infant's experience in the 6 hours prior to specimen collection. Adjusted mixed-effect regression models were used to assess the association between each NISS score and salivary cortisol. RESULTS: Acute and total NISS scores were significantly associated with salivary cortisol level (P = 0.002 and 0.05, respectively). The chronic NISS score was not associated with salivary cortisol levels. Caffeine treatment and postmenstrual age of the infant were important covariates in all models. CONCLUSION: Acute and total NISS score are associated with salivary cortisol level in hospitalized moderately preterm infants.

Secondhand smoke exposure and higher blood pressure in children and adolescents participating in NHANES.

We assessed the relationship between acute and intermittent secondhand tobacco smoke (SHS) exposure with child and adolescent blood pressure (BP). We analyzed cross-sectional data from 3579 children and adolescents aged 8-17 years participating in the National Health and Nutrition Examination Survey (NHANES) collected between 2007 and 2012, with SHS exposure assessed via serum cotinine (a biomarker for acute exposures) and urine NNAL (4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol, a biomarker for intermittent exposures). BP percentiles and z-scores were calculated according to the 2017 guidelines established by the American Academy of Pediatrics. We used weighted linear regression accounting for the complex sampling weights from NHANES and adjusting for socio-demographic and clinical characteristics. Overall, 56% of the children were non-Hispanic white with a mean age of 12.6 years. There was approximately equal representation of boys and girls. Approximately 15.9% of participants lived in homes where smoking was present. In adjusted models, an interquartile range (IQR) increase in urinary NNAL was associated with 0.099 (95% CI: 0.033, 0.16) higher diastolic blood pressure (DBP) z-score, and with a 0.094 (95% CI: 0.011, 0.18) higher systolic blood pressure (SBP) z-score. The odds of being in the hypertensive range was 1.966 (95% CI: 1.31, 2.951) times greater among children with high NNAL exposures compared to those with undetectable NNAL. For serum cotinine, an IQR increase was associated with 0.097 (95% CI: 0.020, 0.17) higher DBP z-scores, but was not significantly associated with SBP z-scores. The associations of cotinine and NNAL with BP also differed by sex. Our findings provide the first characterization of the relationship between a major tobacco-specific metabolite, NNAL, and BP z-scores in a nationally representative population of US children.

Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.

The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.

Unhealthy Behaviors, Prevention Measures, and Neighborhood Cardiovascular Health: A Machine Learning Approach.

This study identifies and ranks predictors of cardiovascular health at the neighborhood level in the United States. We merged the 500 Cities Data and the 2011-2015 American Community Survey to create a new data set that includes sociodemographic characteristics, health behaviors, prevention measures, and cardiovascular health outcomes for more than 28 000 census tracts in the United States. We used random forest to rank predictors of coronary heart disease and stroke. For coronary heart disease, the top 5 ordered predictors were the prevalence of taking medicine for high blood pressure control, binge drinking, being aged 65 years or older, lack of leisure-time physical activity, and obesity. For stroke, the top 5 ordered predictors were the prevalence of obesity, lack of leisure-time physical activity, taking medicine for high blood pressure, being black, and binge drinking. Machine learning approaches have the potential to inform policy makers on important resource allocation decisions at the neighborhood level.

Bayesian varying coefficient kernel machine regression to assess neurodevelopmental trajectories associated with exposure to complex mixtures.

Exposure to environmental mixtures can exert wide-ranging effects on child neurodevelopment. However, there is a lack of statistical methods that can accommodate the complex exposure-response relationship between mixtures and neurodevelopment while simultaneously estimating neurodevelopmental trajectories. We introduce Bayesian varying coefficient kernel machine regression (BVCKMR), a hierarchical model that estimates how mixture exposures at a given time point are associated with health outcome trajectories. The BVCKMR flexibly captures the exposure-response relationship, incorporates prior knowledge, and accounts for potentially nonlinear and nonadditive effects of individual exposures. This model assesses the directionality and relative importance of a mixture component on health outcome trajectories and predicts health effects for unobserved exposure profiles. Using contour plots and cross-sectional plots, BVCKMR also provides information about interactions between complex mixture components. The BVCKMR is applied to a subset of data from PROGRESS, a prospective birth cohort study in Mexico city on exposure to metal mixtures and temporal changes in neurodevelopment. The mixture include metals such as manganese, arsenic, cobalt, chromium, cesium, copper, lead, cadmium, and antimony. Results from a subset of Programming Research in Obesity, Growth, Environment and Social Stressors data provide evidence of significant positive associations between second trimester exposure to copper and Bayley Scales of Infant and Toddler Development cognition score at 24 months, and cognitive trajectories across 6-24 months. We also detect an interaction effect between second trimester copper and lead exposures for cognition at 24 months. In summary, BVCKMR provides a framework for estimating neurodevelopmental trajectories associated with exposure to complex mixtures.

Risk factors associated with multiple correlated health outcomes in the 500 Cities Project.

Reducing chronic disease is a major health challenge. Risk factors for chronic diseases are often studied at the individual level, even though interventions and policies may be implemented at the city level. We use an ecologic study design with city-level data, to simultaneously assess the relative impact of unhealthy behaviors and preventive care measures on multiple chronic disease health outcomes. We analyze a newly available, large national dataset called the 500 Cities Project. We examine the associations between city-level prevalence of unhealthy behaviors, clinical preventive service use, and all chronic disease health outcomes in 500 of the largest U.S. cities for year 2014. After adjusting for age and demographic characteristics, using MANOVA we found that the top three risk factors for all health outcomes are smoking (Pillai's trace = 0.95, approx. F = 688.7, p-value < 0.0001), lack of physical activity (Pillai's trace = 0.91, approx. F = 380.0, p-value < 0.0001) and binge drinking (Pillai's trace = 0.91, approx. F = 348.8, p-value < 0.0001), which are statistically significant after adjusting for multiple comparisons. Higher prevalence of an annual dental checkup, a preventive service use measure, is correlated with lower prevalence of several chronic diseases such as diabetes (correlation coefficient r = -0.88), poor physical health (r = -0.91), stroke (r = -0.85), cardiovascular disease (r = -0.83) and poor mental health (r = -0.82). Identifying important chronic disease risk factors at the city-level may provide more actionable information for policymakers to improve urban health.

Exploratory Cluster Analysis to Identify Patterns of Chronic Kidney Disease in the 500 Cities Project.

Chronic kidney disease is a leading cause of death in the United States. We used cluster analysis to explore patterns of chronic kidney disease in 500 of the largest US cities. After adjusting for socio-demographic characteristics, we found that unhealthy behaviors, prevention measures, and health outcomes related to chronic kidney disease differ between cities in Utah and those in the rest of the United States. Cluster analysis can be useful for identifying geographic regions that may have important policy implications for preventing chronic kidney disease.

Modeling the health effects of time-varying complex environmental mixtures: Mean field variational Bayes for lagged kernel machine regression.

There is substantial interest in assessing how exposure to environmental mixtures, such as chemical mixtures, affect child health. Researchers are also interested in identifying critical time windows of susceptibility to these complex mixtures. A recently developed method, called lagged kernel machine regression (LKMR), simultaneously accounts for these research questions by estimating effects of time-varying mixture exposures, and identifying their critical exposure windows. However, LKMR inference using Markov chain Monte Carlo methods (MCMC-LKMR) is computationally burdensome and time intensive for large datasets, limiting its applicability. Therefore, we develop a mean field variational Bayesian inference procedure for lagged kernel machine regression (MFVB-LKMR). The procedure achieves computational efficiency and reasonable accuracy as compared with the corresponding MCMC estimation method. Updating parameters using MFVB may only take minutes, while the equivalent MCMC method may take many hours or several days. We apply MFVB-LKMR to PROGRESS, a prospective cohort study in Mexico. Results from a subset of PROGRESS using MFVB-LKMR provide evidence of significant positive association between second trimester cobalt levels and z-scored birthweight. This positive association is heightened by cesium exposure. MFVB-LKMR is a promising approach for computationally efficient analysis of environmental health datasets, to identify critical windows of exposure to complex mixtures.