Trends in Healthcare Service Disruptions and Associations with COVID-19 Outcomes among Patients with SMI vs. Non-SMI during COVID-19.

Objective: The study aims to quantify differential changes in outpatient mental health service utilization among 3,724,348 individuals, contrasting those with Severe Mental Illness (SMI) to those without, in the context of the COVID-19 pandemic. Design & Setting: A retrospective cohort study was conducted, utilizing data from Healthix, the second-largest health information exchange in the U.S. Participants: The study population included 3,134,959 Non-SMI patients (84.2%), 355,397 SMI patients (9.5%), and 149,345 Recurrent SMI Patients (4.0%). Exposures: The primary exposure was the COVID-19 pandemic, with a focus on its impact on outpatient mental health services. Main Outcomes and Measures: The primary outcome was the rate of utilization of outpatient mental health services. Secondary outcomes included COVID-19 infection rates and vaccination rates among the study cohorts. Results: Among the non-SMI patients, there was a 30% decline in emergency visits from 650,000 pre-COVID to 455,000 post-COVID (OR=0.70, p < 0.001), and outpatient visits decreased by 50% from 1.2 million to 600,000 (OR=0.50, p = 0.002). In contrast, the SMI group witnessed a 20% reduction in outpatient visits from 120,000 to 96,000 (OR=0.80, p = 0.015) and a 40% decrease in inpatient visits from 50,000 to 30,000 (OR=0.60, p = 0.008). Recurrent SMI patients exhibited a 25% decline in emergency visits from 32,000 to 24,000 (OR=0.75, p = 0.03) and a 35% drop in outpatient visits from 40,000 to 26,000 (OR=0.65, p = 0.009).The pandemic influenced the type of disorders diagnosed. Non-SMI patients experienced a 23% rise in anxiety-related disorders (n=80,000, OR=1.23, p = 0.01) and an 18% increase in stress-related disorders (n=70,000, OR=1.18, p = 0.04). SMI patients had a 15% surge in severe anxiety disorders (n=9,000, OR=1.15, p = 0.02) and a 12% uptick in substance-related disorders (n=7,200, OR=1.12, p = 0.05). Recurrent SMI patients showed a 20% increase in anxiety and adjustment disorders (n=6,400, OR=1.20, p = 0.03).SMI patients were more adversely affected by COVID-19, with a higher infection rate of 7.8% (n=45,972) compared to 4.2% (n=131,669) in non-SMI patients (OR=1.88, p < 0.001). Hospitalization rates also followed this trend, with 5.2% (n=30,648) of SMI patients being hospitalized compared to 3.7% (n=115,995) among non-SMI patients (OR=1.41, p = 0.007). Moreover, SMI patients had lower vaccination rates of 45.6% (n=268,888) versus 58.9% (n=1,844,261) among non-SMI patients (OR=0.77, p = 0.019). Conclusions: In conclusion, our findings reveal significant disparities in healthcare service utilization between individuals with Serious Mental Illness (SMI) and those without. Notably, the SMI cohort experienced greater disruptions in service continuity, with a more pronounced decline in both outpatient and inpatient visits. Furthermore, the types of disorders diagnosed among this group also saw a shift, emphasizing the need for specialized care and attention during times of crisis. The higher rates of COVID-19 infection and hospitalization among SMI patients compared to non-SMI patients underscore the urgency of targeted public health interventions for this vulnerable group. The lower vaccination rates in the SMI cohort highlight another layer of healthcare disparity that needs to be urgently addressed. These findings suggest that the pandemic has amplified pre-existing inequalities in healthcare access and outcomes for individuals with SMI, calling for immediate, evidence-based interventions to mitigate these effects and ensure equitable healthcare service provision.

Trends in psychiatric diagnoses by COVID-19 infection and hospitalization among patients with and without recent clinical psychiatric diagnoses in New York city from March 2020 to August 2021.

Determining emerging trends of clinical psychiatric diagnoses among patients infected with the SARS-CoV-2 virus is important to understand post-acute sequelae of SARS-CoV-2 infection or long COVID. However, published reports accounting for pre-COVID psychiatric diagnoses have usually relied on self-report rather than clinical diagnoses. Using electronic health records (EHRs) among 2,358,318 patients from the New York City (NYC) metropolitan region, this time series study examined changes in clinical psychiatric diagnoses between March 2020 and August 2021 with month as the unit of analysis. We compared trends in patients with and without recent pre-COVID clinical psychiatric diagnoses noted in the EHRs up to 3 years before the first COVID-19 test. Patients with recent clinical psychiatric diagnoses, as compared to those without, had more subsequent anxiety disorders, mood disorders, and psychosis throughout the study period. Substance use disorders were greater between March and August 2020 among patients without any recent clinical psychiatric diagnoses than those with. COVID-19 positive patients (both hospitalized and non-hospitalized) had greater post-COVID psychiatric diagnoses than COVID-19 negative patients. Among patients with recent clinical psychiatric diagnoses, psychiatric diagnoses have decreased since January 2021, regardless of COVID-19 infection/hospitalization. However, among patients without recent clinical psychiatric diagnoses, new anxiety disorders, mood disorders, and psychosis diagnoses increased between February and August 2021 among all patients (COVID-19 positive and negative). The greatest increases were anxiety disorders (378.7%) and mood disorders (269.0%) among COVID-19 positive non-hospitalized patients. New clinical psychosis diagnoses increased by 242.5% among COVID-19 negative patients. This study is the first to delineate the impact of COVID-19 on different clinical psychiatric diagnoses by pre-COVID psychiatric diagnoses and COVID-19 infections and hospitalizations across NYC, one of the hardest-hit US cities in the early pandemic. Our findings suggest the need for tailoring treatment and policies to meet the needs of individuals with pre-COVID psychiatric diagnoses.

Clinician Acceptance of Order Sets for Pain Management: A Survey in Two Urban Hospitals.

BACKGROUND: Order sets are a clinical decision support (CDS) tool in computerized provider order entry systems. Order set use has been associated with improved quality of care. Particularly related to opioids and pain management, order sets have been shown to standardize and reduce the prescription of opioids. However, clinician-level barriers often limit the uptake of this CDS modality. OBJECTIVE: To identify the barriers to order sets adoption, we surveyed clinicians on their training, knowledge, and perceptions related to order sets for pain management. METHODS: We distributed a cross-sectional survey between October 2020 and April 2021 to clinicians eligible to place orders at two campuses of a major academic medical center. Survey questions were adapted from the widely used framework of Unified Theory of Acceptance and Use of Technology. We hypothesize that performance expectancy (PE) and facilitating conditions (FC) are associated with order set use. Survey responses were analyzed using logistic regression. RESULTS: The intention to use order sets for pain management was associated with PE to existing order sets, social influence (SI) by leadership and peers, and FC for electronic health record (EHR) training and function integration. Intention to use did not significantly differ by gender or clinician role. Moderate differences were observed in the perception of the effort of, and FC for, order set use across gender and roles of clinicians, particularly emergency medicine and internal medicine departments. CONCLUSION: This study attempts to identify barriers to the adoption of order sets for pain management and suggests future directions in designing and implementing CDS systems that can improve order sets adoption by clinicians. Study findings imply the importance of order set effectiveness, peer influence, and EHR integration in determining the acceptability of the order sets.

Strategies to optimize comprehension of numerical medication instructions: A systematic review and concept map.

OBJECTIVE: To develop evidence-based recommendations for improving comprehension of quantitative medication instructions. METHODS: This review included a literature search from inception to November 2021. Studies were included for the following: 1) original research; 2) compared multiple formats for presenting quantitative medication information on dose, frequency, and/or time; 3) included patients/lay-people; 4) assessed comprehension-related outcomes quantitatively. To classify the studies, we developed a concept map. We weighed 3 factors (risk of bias in individual studies, consistency of findings among studies, and homogeneity of the interventions tested) to generate 3 levels of recommendations. RESULTS: Twenty-one studies were included. Level 1 recommendations are: 1) use visualizations of medication doses for liquid medications, and 2) express instructions in time-periods rather than times per day. Level 2 recommendations include: validate icons, use panels or tables with explanatory text, use visualizations for non-English speaking populations and for those with low health literacy and limited English proficiency. CONCLUSIONS: Visualized liquid medication doses and time period-based administration instructions improve comprehension of numerical medication instructions. Use of visualizations for those with limited health literacy and English proficiency could result in improved outcomes. PRACTICE IMPLICATIONS: Practitioners should use visualizations for liquid medication instructions and time period-based instructions to improve outcomes.

Taxonomies for synthesizing the evidence on communicating numbers in health: Goals, format, and structure.

Many people, especially those with low numeracy, are known to have difficulty interpreting and applying quantitative information to health decisions. These difficulties have resulted in a rich body of research about better ways to communicate numbers. Synthesizing this body of research into evidence-based guidance, however, is complicated by inconsistencies in research terminology and researcher goals. In this article, we introduce three taxonomies intended to systematize terminology in the literature, derived from an ongoing systematic literature review. The first taxonomy provides a systematic nomenclature for the outcome measures assessed in the studies, including perceptions, decisions, and actions. The second taxonomy is a nomenclature for the data formats assessed, including numbers (and different formats for numbers) and graphics. The third taxonomy describes the quantitative concepts being conveyed, from the simplest (a single value at a single point in time) to more complex ones (including a risk-benefit trade-off and a trend over time). Finally, we demonstrate how these three taxonomies can be used to resolve ambiguities and apparent contradictions in the literature.

A cluster-based model of COVID-19 transmission dynamics.

Many countries have manifested COVID-19 trajectories where extended periods of constant and low daily case rate suddenly transition to epidemic waves of considerable severity with no correspondingly drastic relaxation in preventive measures. Such solutions are outside the scope of classical epidemiological models. Here, we construct a deterministic, discrete-time, discrete-population mathematical model called cluster seeding and transmission model, which can explain these non-classical phenomena. Our key hypothesis is that with partial preventive measures in place, viral transmission occurs primarily within small, closed groups of family members and friends, which we label as clusters. Inter-cluster transmission is infrequent compared with intra-cluster transmission but it is the key to determining the course of the epidemic. If inter-cluster transmission is low enough, we see stable plateau solutions. Above a cutoff level, however, such transmission can destabilize a plateau into a huge wave even though its contribution to the population-averaged spreading rate still remains small. We call this the cryptogenic instability. We also find that stochastic effects when case counts are very low may result in a temporary and artificial suppression of an instability; we call this the critical mass effect. Both these phenomena are absent from conventional infectious disease models and militate against the successful management of the epidemic.

Extracting social determinants of health from electronic health records using natural language processing: a systematic review.

OBJECTIVE: Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. MATERIALS AND METHODS: A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. RESULTS: Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). CONCLUSION: NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

Imprecision and Preferences in Interpretation of Verbal Probabilities in Health: a Systematic Review.

INTRODUCTION: Many health providers and communicators who are concerned that patients will not understand numbers instead use verbal probabilities (e.g., terms such as "rare" or "common") to convey the gist of a health message. OBJECTIVE: To assess patient interpretation of and preferences for verbal probability information in health contexts. METHODS: We conducted a systematic review of literature published through September 2020. Original studies conducted in English with samples representative of lay populations were included if they assessed health-related information and elicited either (a) numerical estimates of verbal probability terms or (b) preferences for verbal vs. quantitative risk information. RESULTS: We identified 33 original studies that referenced 145 verbal probability terms, 45 of which were included in at least two studies and 19 in three or more. Numerical interpretations of each verbal term were extremely variable. For example, average interpretations of the term "rare" ranged from 7 to 21%, and for "common," the range was 34 to 71%. In a subset of 9 studies, lay estimates of verbal probability terms were far higher than the standard interpretations established by the European Commission for drug labels. In 10 of 12 samples where preferences were elicited, most participants preferred numerical information, alone or in combination with verbal labels. CONCLUSION: Numerical interpretation of verbal probabilities is extremely variable and does not correspond well to the numerical probabilities established by expert panels. Most patients appear to prefer quantitative risk information, alone or in combination with verbal labels. Health professionals should be aware that avoiding numeric information to describe risks may not match patient preferences, and that patients interpret verbal risk terms in a highly variable way.

Impact of reproduction number on the multiwave spreading dynamics of COVID-19 with temporary immunity: A mathematical model.

OBJECTIVES: The recent discoveries of phylogenetically confirmed COVID-19 reinfection cases worldwide, together with studies suggesting that antibody titres decrease over time, raise the question of what course the epidemic trajectories may take if immunity were really to be temporary in a significant fraction of the population. The objective of this study is to obtain an answer for this important question. METHODS: We construct a ground-up delay differential equation model tailored to incorporate different types of immune response. We considered two immune responses: (a) short-lived immunity of all types, and (b) short-lived sterilizing immunity with durable severity-reducing immunity. RESULTS: Multiple wave solutions to the model are manifest for intermediate values of the reproduction number R; interestingly, for sufficiently low as well as sufficiently high R, we find conventional single-wave solutions despite temporary immunity. CONCLUSIONS: The versatility of our model, and its very modest demands on computational resources, ensure that a set of disease trajectories can be computed virtually on the same day that a new and relevant immune response study is released. Our work can also be used to analyse the disease dynamics after a vaccine is certified for use and information regarding its immune response becomes available.