The Effect of COVID-19 on Mood Disorders in Urban and Suburban Detroit.

Introduction: The COVID-19 pandemic has increased the global experience of anxiety and depression owing to social isolation and government-mandated quarantine for transmission reduction. To date, literature surrounding the mental health effects of COVID-19 for the U.S. population is limited. Methods: This is a retrospective study from a large metropolitan Detroit health system. Patient encounters between December 23, 2018 and June 22, 2021, with March 23, 2020 being the start of Michigan state-wide lockdown, were used to define pre- and post-COVID-19 encounters, respectively. The data were divided into Detroit and non-Detroit on the basis of patient ZIP code. All patients aged ≥13 years with a visit with a family medicine provider were included. Outcome variables included Patient Health Questionnaires-2 and -9 and General Anxiety Disorder-7 scores; diagnoses of depression, anxiety, adjustment, and grief disorders; antidepressant prescriptions; and behavioral health referrals. Logistic regression was used to determine the incidence of composite mood disorder, depression, and anxiety. Results: A total of 20,970 individuals were included in this study: 10,613 in the Detroit subgroup and 10,357 in the non-Detroit subgroup. A total of 88.2% of the Detroit population were Black, and 70% were female. Logistic regression shows that the incidence of composite mood disorder decreased with increasing age (OR=0.787, 0.608, 0.422, and 0.392; p<0.001). Male sex is a protective factor (OR=0.646, p<0.001). Federal insurance is the only factor presenting a statistically significant increased risk (OR=1.395, p<0.001). There was no statistical difference between residing in urban and suburban areas in the incidence of composite mood disorder (OR=0.996, p=0.953). Conclusions: This research demonstrates that residing in an urban setting did not increase the risk of developing a mental health disorder during the COVID-19 period.

Epidemiology of RSV in Adults and Children with Medically-Attended Acute Respiratory Illness over Three Seasons.

BACKGROUND: Data on the true prevalence of RSV among medically-attended acute respiratory illnesses (MAARI) has been limited by the lack of regular clinical testing of mild to moderate illnesses. Here we present a prospective evaluation of the epidemiology of RSV-associated MAARI across age groups and multimorbidity status over three seasons, which is informative in light of the recommendations for shared decision-making for vaccination in older adults. METHODS: Ambulatory patients ≥6 months of age meeting a common MAARI case definition were prospectively enrolled in the Michigan Ford Influenza Vaccine Effectiveness (MFIVE) study, a subsite of the US Influenza Vaccine Effectiveness Network. All participants were tested by nasal-throat swab for RSV and influenza, including subtype, independently from clinician-directed testing. Participant illness characteristics and calculated Multimorbidity-Weighted Index (MWI) were collected by in-person survey and electronic medical record review. RESULTS: Over three surveillance seasons (fall 2017 to spring 2020), 9.9% (n=441) of 4,442 participants had RSV detected. RSV-associated MAARI was more prevalent than influenza for participants 6 months-4 years of age. Adults with RSV-MAARI had higher median MWI scores overall compared to influenza-MAARI and controls with neither virus (1.62, 0.40, and 0.64, respectively). CONCLUSIONS: RSV is a significant, underrecognized cause of MAARI in both children and adults presenting for ambulatory care. Multimorbidity is an important contributor to RSV-associated MAARI in outpatient adults, providing information to support shared clinical decision-making for vaccination.

The Impact of Environmental Benzene, Toluene, Ethylbenzene, and Xylene Exposure on Blood-Based DNA Methylation Profiles in Pregnant African American Women from Detroit.

African American women in the United States have a high risk of adverse pregnancy outcomes. DNA methylation is a potential mechanism by which exposure to BTEX (benzene, toluene, ethylbenzene, and xylenes) may cause adverse pregnancy outcomes. Data are from the Maternal Stress Study, which recruited African American women in the second trimester of pregnancy from February 2009 to June 2010. DNA methylation was measured in archived DNA from venous blood collected in the second trimester. Trimester-specific exposure to airshed BTEX was estimated using maternal self-reported addresses and geospatial models of ambient air pollution developed as part of the Geospatial Determinants of Health Outcomes Consortium. Among the 64 women with exposure and outcome data available, 46 differentially methylated regions (DMRs) were associated with BTEX exposure (FDR adjusted p-value < 0.05) using a DMR-based epigenome-wide association study approach. Overall, 89% of DMRs consistently exhibited hypomethylation with increasing BTEX exposure. Biological pathway analysis identified 11 enriched pathways, with the top 3 involving gamma-aminobutyric acid receptor signaling, oxytocin in brain signaling, and the gustation pathway. These findings highlight the potential impact of BTEX on DNA methylation in pregnant women.

Development and validation of algorithms for identifying lines of therapy in multiple myeloma using real-world data.

Aim: To validate algorithms based on electronic health data to identify composition of lines of therapy (LOT) in multiple myeloma (MM). Materials & methods: This study used available electronic health data for selected adults within Henry Ford Health (Michigan, USA) newly diagnosed with MM in 2006-2017. Algorithm performance in this population was verified via chart review. As with prior oncology studies, good performance was defined as positive predictive value (PPV) ≥75%. Results: Accuracy for identifying LOT1 (N = 133) was 85.0%. For the most frequent regimens, accuracy was 92.5-97.7%, PPV 80.6-93.8%, sensitivity 88.2-89.3% and specificity 94.3-99.1%. Algorithm performance decreased in subsequent LOTs, with decreasing sample sizes. Only 19.5% of patients received maintenance therapy during LOT1. Accuracy for identifying maintenance therapy was 85.7%; PPV for the most common maintenance therapy was 73.3%. Conclusion: Algorithms performed well in identifying LOT1 - especially more commonly used regimens - and slightly less well in identifying maintenance therapy therein.

Electronic health data helps us understand treatment in the 'real world'. The data has great value in cancer if we can identify the drugs patients get. Yet this is hard in multiple myeloma (MM), where treatment is complex. Algorithms (set of decision rules) to identify drugs can help here. We tested an existing algorithm for identifying 'lines of therapy' (LOT) given to patients with MM. Each LOT included one or more drugs for MM. We also developed and tested a new algorithm for 'maintenance therapy'. This is a treatment given to help maintain the response to the main MM treatment. We tested how well the algorithms identified MM treatments in electronic health data. This data came from Henry Ford Health, a healthcare system in Michigan, USA. Treatments were confirmed by cancer specialists who reviewed medical charts. The LOT algorithm was good at finding the first LOT patients. The maintenance algorithm did a fair job of identifying the most used therapy. Our algorithms could help researchers study the real-world treatment of MM.