Addressing Health Disparities and Social Determinants of Health

Latent space models such as the stochastic block model and the random dot product graph are popular ways of modeling single-layer networks. However, their application to more complex network structures has not received a lot of attention so far. Mac Donald et al. [2021] made a substantial step in this direction by introducing the MultiNeSS model allowing extraction of a latent space component shared by a sample of multiplex networks: multiple, heterogeneous networks observed on a shared node set together. However, this work has an apparent limitation arising from the fact that groups of networks within this sample may have individual group structures besides the one common for the whole sample. Such group stratification may arise when for each network in a sample we additionally observe a categorical attribute, e.g. together with the patient's protein-protein-interaction (PPI) network we can have access to their gender, ethnicity, or age group.
For this more general model that we call GroupMultiNeSS, we establish identifiability, develop a fitting procedure using convex optimization in combination with a nuclear norm penalty, and prove a guarantee of recovery for the latent positions as long as there is sufficient separation between the shared, group-specific, and individual latent subspaces. We compare the model with the original MultiNeSS model in various synthetic scenarios and observe the apparent improvement in the modeling accuracy when the signal strength of the group components is comparable to the one of the shared component.
 

This study assesses the potential long-term environmental effects of redlining policies (1935-1974) on present-day PM2.5 air pollution levels. Enacted in the 1930s, there are only a few low-quality pre-treatment covariates recorded in survey. Consequently, traditional methods fails to sufficiently account for unmeasured confounders, potentially skewing causal interpretations. Moreover, the time lapse of 75 years between the policy action and the pollution measurement further obscures causal links. By integrating historical redlining data with 2010 PM2.5 levels, our study aims to discern whether a causal link exists. Our study addresses challenges with a novel spatial latent framework, using the income level and percentage of Black population in survey as proxies to reconstruct pre-treatment socio-economic factors. We establish identification of a causal effect under broad assumptions, and use Bayesian MCMC to quantify uncertainty. Our method promises to enhance the validity of causal claims by rigorously adjusting for confounders. Anticipated findings will illuminate the effects of redlining policy on contemporary air quality. 

There has been extensive research conducted on the transmission dynamics of COVID-19 disease. The SARS-CoV-2 virus primarily spreads through the respiratory tract. It is very common for the virus to spread rapidly during the incubation period. Further, asymptomatic carriers contribute to this rapid transmission. As an early detection method, wastewater surveillance can be used to detect viruses before they spread far and wide. Our study focused on collecting wastewater samples from treatment plants across various cities in North Dakota. Utilizing viral RNA copies, we compared the model predictions of K-Nearest Neighbor (KNN) regression, Quantile Regression (QR), and Long-Short-Term-Memory (LSTM) network models. To gauge its efficacy, we compared our models' predictions with those of the fundamental Susceptible-Infected-Recovered (SIR) model. 

Disparities in preterm and small for gestational age (SGA) among racial/ethnic groups are public health challenges in the U.S. The Comprehensive Prenatal Care Index (CPCI) was developed to assess the impact of prenatal care. We analyzed PRAMS data from 139,243 participants between 2016 and 2021. The CPCI was validated via Rasch modeling and analyzed with SAS PROC SURVEY, adjusting for demographic and health variables. The CPCI significantly influenced preterm, intermediate, and adequate quality care, increasing the odds of full-term birth by 56% and 91.3%, respectively. For SGA, intermediate care raised odds to 1.093 and adequate care to 1.149. Demonstrating uniform benefits across races/ethnicities, the CPCI highlights the significance of comprehensive prenatal care. In contrast, the Kotelchuck showed an increase in preterm birth risk only within its 'Adequate Plus' category, while the Kessner provided a mixed protective effect. The CPCI robustly predicts preterm and SGA outcomes, demonstrating that superior prenatal care significantly lowers birth outcome risks. It underscores the significance of comprehensive prenatal care components beyond mere healthcare utilization. 

A basic descriptive question in statistics asks whether there are differences in mean outcomes between groups based on levels of a discrete covariate (e.g., racial disparities in health outcomes, differences in opinion based on political party identification, heterogeneity in educational outcomes for students in urban vs. rural school districts, etc). When this categorical covariate of interest is correlated with other factors related to the outcome, however, direct comparisons may lead to erroneous estimates and invalid inferential conclusions without appropriate adjustment. Propensity score methods to adjust for such confounding are broadly employed with observational data as a tool to achieve covariate balance, but implementing them in settings with complex survey weights remains a relatively less researched question, in particular, when the survey weights may also depend on the group variable of interest. In this work, we focus on the specific case when sample selection depends on the grouping covariate of interest. We propose identification formulas to properly estimate the average controlled difference (ACD) in outcomes between groups, with appropriate weighting for covariate imbalance and generalizability. Via extensive simulation, we show that our proposed methods outperform traditional analytic approaches, with less bias in estimating the ACD, lower mean squared error, and close to nominal coverage rates, particularly in our setting of interest. We present the motivation for these methods and results using data from the National Health and Nutrition Examination Survey (NHANES), investigating the interplay of race and social determinants of health when our interest lies in estimating racial differences in mean telomere length. The NHANES sampling scheme and corresponding survey weights depend on self-reported race. We build a "propensity for race" to properly adjust for other social determinants while characterizing the controlled race effect on telomere length. We find that evidence of racial differences in telomere length between Black and White individuals attenuates after accounting for available socioeconomic status variables in NHANES and after utilizing appropriate propensity score and survey weighting techniques. It is our hope that this work will not only further our understanding of appropriate survey design considerations in this framework, but also make the task of analyzing such data in similar settings more accessible to researchers. Software for these methods have been implemented in a publicly-available R package. 

The US has an unprecedented level of opioid overdose-related mortality due to excessive use of prescription opioids. Peer-driven network interventions may be beneficial. A key assumption is some opioid users act as key players and can influence the behavior of others. We used opioid prescription records to create a social network in Rhode Island. The study population was restricted to patients on stable opioid regimens who used one source of payment and received the same opioid medication from ≥ 3 prescribers. An exponential random graph model (ERGM) was used to examine the relationship between patient attributes and the likelihood of tie formation and logistic regression to assess predictors of high betweenness centrality. After controlling for the main effects in the ERGM model, homophily was associated with age group, method of payment, number and type of opioid prescriptions, mean daily dose, and number of providers seen. The type of opioid and number of prescribers were significant predictors of high betweenness centrality. We conclude that patients who use multiple prescribers with opioid use disorder may promote positive health or disrupt harmful behaviors in networks.