Faculty Bibliography

Access to and utilization of consumer credit remains an understudied social determinant of health. We examined associations between a novel, small-area, multidimensional credit insecurity index (CII), and the prevalence of self-reported frequent mental distress across US cities in 2020. The census tract-level CII was developed by the Federal Reserve Bank of New York using Census population information and a nationally representative sample of anonymized Equifax credit report data. The CII was calculated for tracts in 766 cities displayed on the City Health Dashboard at the time of analysis, predominantly representing cities with over 50,000 residents. The CII combined data on tract-level participation in the formal credit economy with information on the percent of individuals without revolving credit, percent with high credit utilization, and percent with deep subprime credit scores. Tracts were classified as credit-assured, credit-likely, mid-tier, at-risk, or credit-insecure. We used linear regression to examine associations between the CII and a modeled tract-level measure of frequent mental distress, obtained from the CDC PLACES project. Regression models were adjusted for neighborhood economic and demographic characteristics. We examined effect modification by US region by including two-way interaction terms in regression models. In adjusted models, credit-insecure tracts had a modestly higher prevalence of frequent mental distress (prevalence difference = 0.38 percentage points; 95% CI = 0.32, 0.44), compared to credit-assured tracts. Associations were most pronounced in the Midwest. Local factors impacting credit access and utilization are often modifiable. The CII, a novel indicator of community financial well-being, may be an independent predictor of neighborhood health in US cities and could illuminate policy targets to improve access to desirable credit products and downstream health outcomes.

BACKGROUND:Maternal stress has negative consequences on infant behavioral development, and COVID-19 presented uniquely stressful situations to mothers of infants born during the pandemic. We hypothesized that mothers with higher levels of perceived stress during the pandemic would report higher levels of infant regulatory problems including crying and interrupted sleep patterns. METHODS:As part 6 sites of a longitudinal study, mothers of infants born during the pandemic completed the Perceived Stress Scale, the Brief Infant Sleep Questionnaire, and an Infant Crying survey at 6 (n = 433) and 12 (n = 344) months of infant age. RESULTS:Maternal perceived stress, which remained consistent at 6 and 12 months of infant age, was significantly positively correlated with time taken to settle infants. Although maternal perceived stress was not correlated with uninterrupted sleep length, time taken to put the infant to sleep was correlated. Perceived stress was also correlated with the amount of infant crying and fussiness reported at 6 months. CONCLUSIONS:Mothers who reported higher levels of perceived stress during the pandemic reported higher levels of regulatory problems, specifically at 6 months. Examining how varying levels of maternal stress and infant behaviors relate to overall infant developmental status over time is an important next step. IMPACT/CONCLUSIONS:Women giving birth during the COVID-19 pandemic who reported higher levels of stress on the Perceived Stress Scale also reported higher levels of infant fussiness and crying at 6 months old, and more disruptive sleep patterns in their infants at 6 months and 12 months old. Sleeping problems and excessive crying in infancy are two regulatory problems that are known risk factors for emotional and behavioral issues in later childhood. This paper is one of the first studies highlighting the associations between maternal stress and infant behaviors during the COVID-19 pandemic.

Air pollution, especially exposure to particulate matter 2.5 (PM_2.5), has been associated with an increase in morbidity and mortality around the world. Specifically, it seems that PM_2.5 promotes the development of cardiovascular risk factors such as hypertension and atherosclerosis, while being associated with an increased risk of cardiovascular diseases, including myocardial infarction (MI), stroke, heart failure, and arrhythmias. In this review, we seek to elucidate the pathophysiological mechanisms by which exposure to PM_2.5 can result in adverse cardiovascular outcomes, in addition to understanding the link between exposure to PM_2.5 and cardiovascular events. It is hypothesized that PM_2.5 functions via 3 mechanisms: increased oxidative stress, activation of the inflammatory pathway of the immune system, and stimulation of the autonomic nervous system which ultimately promote endothelial dysfunction, atherosclerosis, and systemic inflammation that can thus lead to cardiovascular events. It is important to note that the various cardiovascular associations of PM_2.5 differ regarding the duration of exposure (short vs long) to PM_2.5, the source of PM_2.5, and regulations regarding air pollution in the area where PM_2.5 is prominent. Current strategies to reduce PM_2.5 exposure include personal strategies such as avoiding high PM_2.5 areas such as highways or wearing masks outdoors, to governmental policies restricting the amount of PM_2.5 produced by organizations. This review, by highlighting the significant impact between PM_2.5 exposure and cardiovascular health will hopefully bring awareness and produce significant change regarding dealing with PM_2.5 levels worldwide.

A more granular donor kidney grading scale, the kidney donor profile index (KDPI), has recently emerged in contradistinction to the standard criteria donor/expanded criteria donor framework. In this paper, we built a Markov decision process model to evaluate the survival, quality-adjusted life years (QALY), and cost advantages of using high-KDPI kidneys based on multiple KDPI strata over a 60-month time horizon as opposed to remaining on the waiting list waiting for a lower-KDPI kidney. Data for the model were gathered from the Scientific Registry of Transplant Recipients and the United States Renal Data System Medicare parts A, B, and D databases. Of the 129,024 phenotypes delineated in this model, 65% of them would experience a survival benefit, 81% would experience an increase in QALYs, 87% would see cost-savings, and 76% would experience cost-savings per QALY from accepting a high-KDPI kidney rather than remaining on the waiting list waiting for a kidney of lower-KDPI. Classification and regression tree analysis (CART) revealed the main drivers of increased survival in accepting high-KDPI kidneys were wait time ≥30 months, panel reactive antibody (PRA) <90, age ≥45 to 65, diagnosis leading to renal failure, and prior transplantation. The CART analysis showed the main drivers of increased QALYs in accepting high-kidneys were wait time ≥30 months, PRA <90, and age ≥55 to 65.

BACKGROUND/UNASSIGNED:Advancements in medical technology, healthcare delivery, and organ allocation resulted in improved patient/graft survival for older (age ≥65) kidney transplant (KT) recipients. However, the recent trends in these post-KT outcomes are uncertain in light of the mounting burden of cardiovascular disease, changing kidney allocation policies, heterogeneity in candidates' risk profile, and the coronavirus disease 2019 pandemic. Thus, we examined secular trends in post-KT outcomes among older and younger KT recipients over the last 3 decades. METHODS/UNASSIGNED:We identified 73 078 older and 378 800 younger adult (aged 18-64) recipients using Scientific Registry of Transplant Recipients (1990-2022). KTs were grouped into 6 prepandemic eras and 1 postpandemic-onset era. Kaplan-Meier and Cox proportional hazards models were used to examine temporal trends in post-KT mortality and death-censored graft failure. RESULTS/UNASSIGNED:From 1990 to 2022, a 19-fold increase in the proportion of older KT recipients was observed compared to a 2-fold increase in younger adults despite a slight decline in the absolute number of older recipients in 2020. The mortality risk for older recipients between 2015 and March 14, 2020, was 39% (adjusted hazard ratio [aHR] = 0.61, 95% confidence interval [CI], 0.50-0.75) lower compared to 1990-1994, whereas that for younger adults was 47% lower (aHR = 0.53, 95% CI, 0.48-0.59). However, mortality risk during the pandemic was 25% lower (aHR = 0.75, 95% CI, 0.61-0.93) in older adults and 37% lower in younger adults (aHR = 0.63, 95% CI, 0.56-0.70) relative to 1990-1994. For both populations, the risk of graft failure declined over time and was unaffected during the pandemic relative to the preceding period. CONCLUSIONS/UNASSIGNED:The steady improvements in 5-y mortality and graft survival were disrupted during the pandemic, particularly among older adults. Specifically, mortality among older adults reflected rates seen 20 y prior.

Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estimates is to aggregate estimates from other geographies, for example, from counties or census tracts to Congressional Districts. Doing so requires several methodological decisions. We refine a method to aggregate health metric estimates from one geography to another, using a population weighted approach. The method's accuracy is evaluated by comparing three aggregated metric estimates to metric estimates from the US Census American Community Survey for the same years: Broadband Access, High School Completion, and Unemployment. We then conducted four sensitivity analyses testing: the effect of aggregating counts vs. percentages; impacts of component geography size and data missingness; and extent of population overlap between component and target geographies. Aggregated estimates were very similar to estimates for identical metrics drawn directly from the data source. Sensitivity analyses suggest the following best practices for Congressional district-based metrics: utilizing smaller, more plentiful geographies like census tracts as opposed to larger, less plentiful geographies like counties, despite potential for less stable estimates in smaller geographies; favoring geographies with higher percentage population overlap.

INTRODUCTION/UNASSIGNED:Epidemiological studies commonly use residential addresses at birth to estimate exposures throughout pregnancy, ignoring residential mobility. Lack of consideration for residential mobility during pregnancy might lead to exposure misclassification that should be addressed in environmental epidemiology. METHODS/UNASSIGNED:), temperature, and greenness (Enhanced Vegetation Index [EVI]). RESULTS/UNASSIGNED:) and EVI (range -0.305 to 0.307, average -0.013), but not temperature. Overestimations were significantly larger for mothers with higher socioeconomic status. Our findings indicate that the error for prenatal exposure can occur when residential mobility is not considered and is disproportional by maternal characteristics. CONCLUSIONS/UNASSIGNED:Epidemiological studies should consider residential mobility in exposure assessments based on geolocation when possible, and results based on mother's residence at birth should be interpreted with understanding of potential differential exposure misclassification.

OBJECTIVE:To develop a natural language processing (NLP) algorithm that identifies social determinants of health (SDoH), including housing, transportation, food, and medication insecurities, social isolation, abuse, neglect, or exploitation, and financial difficulties for patients with Alzheimer's disease and related dementias (ADRD) from unstructured electronic health records (EHRs). DATA SOURCES AND STUDY SETTING:We leveraged 1000 medical notes randomly selected from 7401 emergency department and inpatient social worker notes generated between 2015 and 2019 for 231 unique patients diagnosed with ADRD at Michigan Medicine. STUDY DESIGN:We developed a rule-based NLP algorithm for the identification of seven domains of SDoH noted above. We also compared the rule-based algorithm with deep learning and regularized logistic regression approaches. These models were compared using accuracy, sensitivity, specificity, F1 score, and the area under the receiver operating characteristic curve (AUC). All notes were split into 700 notes for training NLP algorithms, and 300 notes for validation. DATA COLLECTION/EXTRACTION METHODS:Social worker notes used in this study were extracted from the Michigan Medicine EHR database. PRINCIPAL FINDINGS:Of the 700 notes for training, F1 and AUC for the rule-based algorithm were at least 0.94 and 0.95, respectively, for all SDoH categories. Of the 300 notes for validation, F1 and AUC were at least 0.80 and 0.97, respectively, for all SDoH except housing and medication insecurities. The deep learning and regularized logistic regression algorithms had unsatisfactory performance. CONCLUSIONS:The rule-based algorithm can accurately extract SDoH information in all seven domains of SDoH except housing and medication insecurities. Findings from the algorithm can be used by clinicians and social workers to proactively address social needs of patients with ADRD and other vulnerable patient populations.

BACKGROUND:Podcasts, or episodic digital audio recordings, represent a novel way to reach large audiences for public education. Genetic evaluation has important implications for prostate cancer (PCa) care but is underutilized. We created a series of five podcasts about PCa genetics and tested their usefulness in raising awareness and providing education to lay audiences. METHODS:We recruited 157 men and women from the general public and 100 patients with PCa from across the U.S., who listened to a podcast and completed an online survey. The primary outcome was the perceived usefulness of the podcast (score ≥5 on a published 7-point Likert scale). Secondary outcomes were relevance to informational needs, satisfaction and ease of use, as well as genetic knowledge and attitudes toward genetic testing after listening to the podcasts. RESULTS:The podcasts were associated with high mean scores for perceived usefulness (5.6/7), relevance to informational needs (5.6/7), satisfaction (5.8/7), and ease of use (5.9/7). After listening to the podcasts, 80-100% correctly answered most key knowledge questions about PCa genetics, and 85% had a positive attitude toward genetic testing. On multivariable analysis, the perceived usefulness of the podcasts was higher among Black/Hispanic adults (p = 0.05) and those with a family history of PCa (p = 0.01). CONCLUSIONS:A podcast series on PCa genetics was perceived as useful and associated with high rates of knowledge for patients with PCa and the general public. Podcasts represent a promising new educational tool to raise awareness about PCa genetic evaluation, particularly for high-risk groups.

OBJECTIVE:To evaluate the association between census-tract level measures of social vulnerability and residential segregation and IPN follow up. METHODS:This retrospective cohort study included patients with IPN ≥6 mm in size or multiple subsolid/ground-glass IPNs <6 mm (with non-optional follow-up recommendations) diagnosed between January 1, 2018 and December 30, 2019 at a large urban tertiary center and followed ≥two years. Geographic sociodemographic context was characterized by 2018 U.S. Centers for Disease Control and Prevention Social Vulnerability Index (SVI) and the Index of Concentration at the Extreme (ICE), categorized in quartiles. Multivariable binomial regression models were utilized with a primary outcome of inappropriate IPN follow up (late or no follow up). Models were also stratified by nodule risk. RESULTS:The study consisted of 2,492 patients (mean age 65.6 years +/- 12.6 years; 1,361 women). Top-quartile SVI patients were more likely to have inappropriate follow up (Risk Ratio [RR]: 1.24, 95% Confidence Interval [95% CI], 1.12-1.36]), compared with the bottom quartile; risk was also elevated in top-quartile SVI subcategories of Socioeconomic Status (RR: 1.23, 95% CI, 1.13-1.34), Minority Status and Language (RR: 1.24, 95% CI, 1.03-1.48), Housing and Transportation (RR: 1.13, 95% CI, 1.02-1.26), and ICE (RR: 1.20, 95% CI, 1.11-1.30). Further, top-quartile ICE was associated with greater risk of inappropriate follow up among high-risk vs. lower-risk IPN (1.33 [1.18-1.50] vs. 1.13 [1.02-1.25]), respectively, P for interaction= 0.017). DISCUSSION/CONCLUSIONS:Local social vulnerability and residential segregation are associated with inappropriate IPN follow up and may inform policy or interventions tailored for neighborhoods.