Faculty Bibliography

For interventions that affect how individuals interact, social network data may aid in understanding the mechanisms through which an intervention is effective. Social networks may even be an intermediate outcome observed prior to end of the study. In fact, social networks may also mediate the effects of the intervention on the outcome of interest, and Sweet (2019) introduced a statistical model for social networks as mediators in network-level interventions. We build on their approach and introduce a new model in which the network is a mediator using a latent space approach. We investigate our model through a simulation study and a real-world analysis of teacher advice-seeking networks.

BACKGROUND:Environmental exposures account for a growing proportion of global mortality. Large cohort studies are needed to characterize the independent impact of environmental exposures on mortality in low-income settings. METHODS:We collected data on individual and environmental risk factors for a multiethnic cohort of 50,045 individuals in a low-income region in Iran. Environmental risk factors included: ambient fine particular matter air pollution; household fuel use and ventilation; proximity to traffic; distance to percutaneous coronary intervention (PCI) center; socioeconomic environment; population density; local land use; and nighttime light exposure. We developed a spatial survival model to estimate the independent associations between these environmental exposures and all-cause and cardiovascular mortality. FINDINGS:Several environmental factors demonstrated associations with mortality after adjusting for individual risk factors. Ambient fine particulate matter air pollution predicted all-cause mortality (per μg/m3, HR 1.20, 95% CI 1.07, 1.36) and cardiovascular mortality (HR 1.17, 95% CI 0.98, 1.39). Biomass fuel use without chimney predicted all-cause mortality (reference = gas, HR 1.23, 95% CI 0.99, 1.53) and cardiovascular mortality (HR 1.36, 95% CI 0.99, 1.87). Kerosene fuel use without chimney predicted all-cause mortality (reference = gas, HR 1.09, 95% CI 0.97, 1.23) and cardiovascular mortality (HR 1.19, 95% CI 1.01, 1.41). Distance to PCI center predicted all-cause mortality (per 10km, HR 1.01, 95% CI 1.004, 1.022) and cardiovascular mortality (HR 1.02, 95% CI 1.004, 1.031). Additionally, proximity to traffic predicted all-cause mortality (HR 1.13, 95% CI 1.01, 1.27). In a separate validation cohort, the multivariable model effectively predicted both all-cause mortality (AUC 0.76) and cardiovascular mortality (AUC 0.81). Population attributable fractions demonstrated a high mortality burden attributable to environmental exposures. INTERPRETATION:Several environmental factors predicted cardiovascular and all-cause mortality, independent of each other and of individual risk factors. Mortality attributable to environmental factors represents a critical opportunity for targeted policies and programs.

There is some, but inconsistent, evidence to suggest that matching patient treatment preference enhances treatment engagement and outcome. The current study examined differential preferences and factors associated with treatment preference for 12-week group cognitive behavioral therapy (CBT), yoga, or stress education in 226 adults with generalized anxiety disorder (GAD; 70% female, Mean age = 33 ± 13.5). In a subsample of 165 patients who reported an intervention preference and were randomized to yoga or CBT, we further examined whether match to preferred intervention improved the primary treatment outcome (responder status on Clinical Global Impressions Scale) and engagement (dropout, homework compliance). Preferences for CBT (44%) and yoga (40%) were similar among patients. Women tended to prefer yoga (OR = 2.75, p = .01) and CBT preference was associated with higher baseline perceived stress (OR = 0.92, p = .04) and self-consciousness meta-cognitions (OR = 0.90, p = .02). Among those not matched to their preference, treatment response was higher for those receiving CBT than yoga (OR = 11.73, p = .013); there were no group differences for those matched to their treatment preference. In yoga, those who received their preference were more likely to drop than those who did not (OR = 3.02, 95% CI = [1.20, 7.58], p = .037). This was not the case for CBT (OR = 0.37, 95% CI = [0.13, 1.03], p = .076). Preference match did not predict homework compliance. Overall, results suggest that treatment preference may be important to consider to optimize outcome and engagement; however, it may vary by treatment modality. Future research incorporating preference, especially with yoga for anxiety, is aligned with personalized medicine. TRIAL REGISTRATION: clinicaltrials.gov: NCT01912287; https://clinicaltrials.gov/ct2/show/NCT01912287.

Background: Mobile health cardiac rehabilitation (mHealth-CR) may improve outcomes among older adults with ischemic heart disease, but variable engagement is not yet understood. We therefore analyzed preliminary data from the RESILIENT trial, an ongoing randomized trial of mHealth-CR vs. usual care in patients age >= 65.
Method(s): Data from the first 50 intervention participants were analyzed. Weekly engagement was scored from 0-11 based on exercise completion, therapist communication, video viewing, and BP self-measurement. Participants were classified as high or low engagement based on median engagement scores. Groups were compared by age, sex, social support (living alone), depression (PHQ-8), and Charlson Comorbidity Index (CCI).
Result(s): There was widely varying engagement with mHealth-CR that ranged from nearly zero to nearly perfect engagement (Figure). There were no significant differences between high and low engagement groups based on mean age (72.6 vs. 72.8, P=0.8), sex (76% male vs. 80%, P=0.7), living alone (28% vs. 44%, P=0.2), depression (mean 3.9 vs. 4.6, P=0.5), or comorbidity burden (mean 4.4 vs. 4.8, P=0.3), although the sample size was small.
Conclusion(s): Our early findings show wide variation in mHealth-CR engagement among older adults

BACKGROUND:Large-scale longitudinal studies evaluating influences of the built environment on risk for type 2 diabetes (T2D) are scarce, and findings have been inconsistent. OBJECTIVE:To evaluate whether land use environment (LUE), a proxy of neighborhood walkability, is associated with T2D risk across different US community types, and to assess whether the association is modified by food environment. METHODS:The Veteran's Administration Diabetes Risk (VADR) study is a retrospective cohort of diabetes-free US veteran patients enrolled in VA primary care facilities nationwide from January 1, 2008, to December 31, 2016, and followed longitudinally through December 31, 2018. A total of 4,096,629 patients had baseline addresses available in electronic health records that were geocoded and assigned a census tract-level LUE score. LUE scores were divided into quartiles, where a higher score indicated higher neighborhood walkability levels. New diagnoses for T2D were identified using a published computable phenotype. Adjusted time-to-event analyses using piecewise exponential models were fit within four strata of community types (higher-density urban, lower-density urban, suburban/small town, and rural). We also evaluated effect modification by tract-level food environment measures within each stratum. RESULTS:In adjusted analyses, higher LUE had a protective effect on T2D risk in rural and suburban/small town communities (linear quartile trend test p-value <0.001). However, in lower density urban communities, higher LUE increased T2D risk (linear quartile trend test p-value <0.001) and no association was found in higher density urban communities (linear quartile trend test p-value = 0.317). Particularly strong protective effects were observed for veterans living in suburban/small towns with more supermarkets and more walkable spaces (p-interaction = 0.001). CONCLUSION/CONCLUSIONS:Among veterans, LUE may influence T2D risk, particularly in rural and suburban communities. Food environment may modify the association between LUE and T2D.

Families of color living in historically disinvested neighborhoods face a multitude of health disparities which have been exacerbated by COVID-19 and the resulting strategies to mitigate its transmission. School closure, which occurred with little warning and few, if any, resources for preparation, disrupted multiple aspects of families' lives; these disruptions are anticipated to adversely impact mental health and well-being. The current study aims to advance understanding of the experiences of families of young children of color during the pandemic by utilizing a natural experiment design to test impact on child and parent mental health and sleep in the context of COVID-19 related school closure among families in historically disinvested neighborhoods. Data from this study come from an ongoing study of 281 families of color enrolled in 41 pre-kindergarten (pre-K) programs in neighborhoods across New York City (NYC). In NYC, school closure occurred on March 16, 2020, during a data collection period involving phone surveys with parents; the quasi-experimental design allows for comparison of the 198 families who had completed the survey prior to March 16, and the 83 families who completed the survey after March 16, using identical protocols and procedures. Results demonstrate poorer mental health among parents surveyed after school closure as compared to before school closure. No differences were found for parent sleep, child mental health, or child sleep. Implications of this work highlight the need for structural and systemic supports for families faced with compounding stressors as a result of the COVID-19 pandemic and related school closure.