Identifying multimorbidity profiles associated with COVID-19 severity in chronic patients using network analysis in the PRECOVID Study

Long term mortality trends in people with severe mental illnesses and how COVID-19, ethnicity and other chronic mental health comorbidities contributed: a retrospective cohort study

BACKGROUND

People with schizophrenia-spectrum and bipolar disorders (severe mental illnesses; 'SMI') experience excess mortality. Our aim was to explore longer-term trends in mortality, including the COVID-19 pandemic period, with a focus on additional vulnerabilities (psychiatric comorbidities and race/ ethnicity) in SMI.

METHODS

Retrospective cohort study using electronic health records from secondary mental healthcare, covering a UK region of 1.3 million people. Mortality trends spanning fourteen years, including the COVID-19 pandemic, were assessed in adults with clinician-ascribed ICD-10 diagnoses for schizophrenia-spectrum and bipolar disorders.

RESULTS

The sample comprised 22 361 people with SMI with median follow-up of 10.6 years. Standardized mortality ratios were more than double the population average pre-pandemic, increasing further during the pandemic, particularly in those with SMI and psychiatric comorbidities. Mortality risk increased steadily among people with SMI and comorbid depression, dementia, substance use disorders and anxiety over 13-years, increasing further during the pandemic. COVID-19 mortality was elevated in people with SMI and comorbid depression (sub-Hazard Ratio: 1.48 [95% CI 1.03-2.13]), dementia (sHR:1.96, 1.26-3.04) and learning disabilities (sHR:2.30, 1.30-4.06), compared to people with only SMI. COVID-19 mortality risk was similar for minority ethnic groups and White British people with SMI. Elevated all-cause mortality was evident in Black Caribbean (adjusted Rate Ratio: 1.40, 1.11-1.77) and Black African people with SMI (aRR: 1.59, 1.07-2.37) during the pandemic relative to earlier years.

CONCLUSIONS

Mortality has increased over time in people with SMI. The pandemic exacerbated pre-existing trends. Actionable solutions are needed which address wider social determinants and address disease silos.

A call for caution when using network methods to study multimorbidity: an illustration using data from the Canadian Longitudinal Study on Aging

OBJECTIVES

To examine the impact of two key choices when conducting a network analysis (clustering methods and measure of association) on the number and type of multimorbidity clusters.

STUDY DESIGN AND SETTING

Using cross-sectional self-reported data on 24 diseases from 30,097 community-living adults aged 45-85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using 5 clustering methods and 11 association measures commonly used in multimorbidity studies. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters, and 1 indicates perfect agreement. We compared the network analysis results to disease clusters independently identified by two clinicians.

RESULTS

Results differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 24, with a low similarity of conditions within clusters. Compared to clinician-derived clusters, ARIs ranged from -0.02 to 0.24, indicating little similarity.

CONCLUSION

These analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, in individual older adults, diseases may not cluster predictably, highlighting the need for a personalized approach to their care.

Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups

BACKGROUND

Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data.

METHODS

We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC.

FINDINGS

Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations.

INTERPRETATION

Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity.

FUNDING

National Institute for Health and Care Research.

Multimorbidity and frailty are associated with poorer SARS-CoV-2-related outcomes: systematic review of population-based studies

BACKGROUND

Estimating the risks and impacts of COVID-19 for different health groups at the population level is essential for orienting public health measures. Adopting a population-based approach, we conducted a systematic review to explore: (1) the etiological role of multimorbidity and frailty in developing SARS-CoV-2 infection and COVID-19-related short-term outcomes; and (2) the prognostic role of multimorbidity and frailty in developing short- and long-term outcomes. This review presents the state of the evidence in the early years of the pandemic. It was conducted within the European Union Horizon 2020 program (No: 101018317); Prospero registration: CRD42021249444.

METHODS

PubMed, Embase, World Health Organisation COVID-19 Global literature on coronavirus disease, and PsycINFO were searched between January 2020 and 7 April 2021 for multimorbidity and 1 February 2022 for frailty. Quantitative peer-reviewed studies published in English with population-representative samples and validated multimorbidity and frailty tools were considered.

RESULTS

Overall, 9,701 records were screened by title/abstract and 267 with full text. Finally, 14 studies were retained for multimorbidity (etiological role, n = 2; prognostic, n = 13) and 5 for frailty (etiological role, n = 2; prognostic, n = 4). Only short-term outcomes, mainly mortality, were identified. An elevated likelihood of poorer outcomes was associated with an increasing number of diseases, a higher Charlson Comorbidity Index, different disease combinations, and an increasing frailty level.

DISCUSSION

Future studies, which include the effects of recent virus variants, repeated exposure and vaccination, will be useful for comparing the possible evolution of the associations observed in the earlier waves.

Impact of COVID-19 pandemic on adults and children with atopic dermatitis and food allergy: Systematic review

Background

The coronavirus disease 2019 (COVID-19) pandemic caused significant disruptions to health care services and health impacts on patients with atopic dermatitis (AD) and/or food allergy (FA).

Objective

We evaluated the impact of the COVID-19 pandemic and disease on AD/FA patients.

Methods

A comprehensive systematic literature search was conducted from December 2019 to 2022. Screening and data extraction were done following the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, and the Mixed Methods Appraisal Tool, or MMAT, was used to assess risk of bias.

Results

In total, 159 studies were included. Five of 7 studies reported no significant changes in overall incidence or prevalence of AD during the pandemic, although some studies noted an increase in the elderly and infants. Telehealth served as an effective alternative to face-to-face consultations, with mixed levels of patient and provider satisfaction. Dissatisfaction was most marked in patients with more severe disease, who thought that their disease was inadequately managed through telemedicine. Higher levels of general anxiety were recorded in both AD/FA patients and caregivers, and it was more pronounced in patients with severe disease. Most studies reported no significant differences in postvaccination adverse effects in AD patients; however, results were more varied in FA patients.

Conclusion

Our review identified the impact of COVID-19 pandemic- and disease-driven changes on AD/FA patients. Telemedicine is uniquely suited to manage atopic diseases, and hybrid care may be a suitable approach even in the postpandemic era. COVID-19 vaccines and biologics can be safely administered to patients with atopic diseases, with appropriate patient education to ensure continued care for high-risk patients.

Change in Code Status Orders of Hospitalized Adults With COVID-19 Throughout the Pandemic: A Retrospective Cohort Study

Aim: Our aim was to examine how code status orders for patients hospitalized with COVID-19 changed over time as the pandemic progressed and outcomes improved. Methods: This retrospective cohort study was performed at a single academic center in the United States. Adults admitted between March 1, 2020, and December 31, 2021, who tested positive for COVID-19, were included. The study period included four institutional hospitalization surges. Demographic and outcome data were collected and code status orders during admission were trended. Data were analyzed with multivariable analysis to identify predictors of code status. Results: A total of 3615 patients were included with full code (62.7%) being the most common final code status order followed by do-not-attempt-resuscitation (DNAR) (18.1%). Time of admission (per every six months) was an independent predictor of final full compared to DNAR/partial code status (p = 0.04). Limited resuscitation preference (DNAR or partial) decreased from over 20% in the first two surges to 10.8% and 15.6% of patients in the last two surges. Other independent predictors of final code status included body mass index (p < 0.05), Black versus White race (0.64, p = 0.01), time spent in the intensive care unit (4.28, p = <0.001), age (2.11, p = <0.001), and Charlson comorbidity index (1.05, p = <0.001). Conclusions: Over time, adults admitted to the hospital with COVID-19 were less likely to have a DNAR or partial code status order with persistent decrease occurring after March 2021. A trend toward decreased code status documentation as the pandemic progressed was observed.

An Unsupervised Cluster Analysis of Post-COVID-19 Mental Health Outcomes and Associated Comorbidities

The COVID-19 pandemic continues to be widespread, and little is known about mental health impacts from dealing with the disease itself. This retrospective study used a deidentified health information exchange (HIE) dataset of electronic health record data from the state of Rhode Island and characterized different subgroups of the positive COVID-19 population. Three different clustering methods were explored to identify patterns of condition groupings in this population. Increased incidence of mental health conditions was seen post-COVID-19 diagnosis, and these individuals exhibited higher prevalence of comorbidities compared to the negative control group. A self-organizing map cluster analysis showed patterns of mental health conditions in half of the clusters. One mental health cluster revealed a higher comorbidity index and higher severity of COVID-19 disease. The clinical features identified in this study motivate the need for more in-depth analysis to predict and identify individuals at high risk for developing mental illness post-COVID-19 diagnosis.

Impact of comorbidities on hospitalised Syrian patients with COVID-19: a retrospective study

OBJECTIVES

This study aims to compare the clinical manifestations, laboratory findings, outcomes and overall survival time of patients with COVID-19 with and without comorbidities.

DESIGN

Retrospective design.

SETTING

This study was undertaken at two hospitals in Damascus.

PARTICIPANTS

A total of 515 Syrian patients met the inclusion criterion, laboratory-confirmed COVID-19 infection following the Centers for Disease Control and Prevention. Exclusion criteria were suspected and probable cases that were not confirmed with a positive reverse transcription-PCR assay, and patients who self-discharged from the hospital against medical advice.

PRIMARY AND SECONDARY OUTCOME MEASURES

First, assess the impacts of comorbidities on COVID-19 infection in four areas (clinical manifestations, laboratory findings, severity and outcomes). Second, calculate the overall survival time for patients with COVID-19 with comorbidities.

RESULTS

Of 515 patients included, 316 (61.4%) were male and 347 (67.4%) had at least one coexisting chronic disease. Patients with comorbidities compared with no comorbidities were more vulnerable to poor outcomes such as severe infection (32.0% vs 9.5%, p<0.001), severe complications (34.6% vs 9.5%, p<0.001), the need for mechanical ventilation (28.8% vs 7.7%, p<0.001) and death (32.0% vs 8.3%, p<0.001). Multiple logistic regression showed that age ≥65 years old, positive smoking history, having ≥2 comorbidities and chronic obstructive pulmonary disease were risk factors linked to severe COVID-19 infection in patients with comorbidities. Overall survival time was lower among patients with comorbidities (vs no comorbidities), patients with ≥2 comorbidities (vs one comorbidity), and patients with hypertension, chronic obstructive pulmonary disease, malignancy or obesity (vs other comorbidities) (p<0.05).

CONCLUSION

This study revealed that COVID-19 infection had poor outcomes among those with comorbidities. Severe complications, mechanical ventilation usage and death were more prevalent among patients with comorbidities compared with those with no comorbidities.

Comorbidities, multimorbidity and COVID-19

The influence of comorbidities on COVID-19 outcomes has been recognized since the earliest days of the pandemic. But establishing causality and determining underlying mechanisms and clinical implications has been challenging-owing to the multitude of confounding factors and patient variability. Several distinct pathological mechanisms, not active in every patient, determine health outcomes in the three different phases of COVID-19-from the initial viral replication phase to inflammatory lung injury and post-acute sequelae. Specific comorbidities (and overall multimorbidity) can either exacerbate these pathological mechanisms or reduce the patient's tolerance to organ injury. In this Review, we consider the impact of specific comorbidities, and overall multimorbidity, on the three mechanistically distinct phases of COVID-19, and we discuss the utility of host genetics as a route to causal inference by eliminating many sources of confounding. Continued research into the mechanisms of disease-state interactions will be crucial to inform stratification of therapeutic approaches and improve outcomes for patients.

Vaccine-based clinical protection against SARS-CoV-2 infection and the humoral immune response: A 1-year follow-up study of patients with multiple sclerosis receiving ocrelizumab

Introduction

Given the varying severity of coronavirus disease 2019 (COVID-19) and the rapid spread of Severe-Acute-Respiratory-Syndrome-Corona-Virus-2 (SARS-CoV-2), vaccine-mediated protection of particularly vulnerable individuals has gained increasing attention during the course of the pandemic.

Methods

We performed a 1-year follow-up study of 51 ocrelizumab-treated patients with multiple sclerosis (OCR-pwMS) who received COVID-19 vaccination in 2021. We retrospectively identified 37 additional OCR-pwMS, 42 pwMS receiving natalizumab, 27 pwMS receiving sphingosine 1-phosphate receptor modulators, 59 pwMS without a disease-modifying therapy, and 61 controls without MS (HC). In OCR-pwMS, anti-SARS-CoV-2(S)-antibody titers were measured prior to the first and after the second, third, and fourth vaccine doses (pv2/3/4). The SARS-CoV-2-specific T cell response was analyzed pv2. SARS-CoV-2 infection status, COVID-19 disease severity, and vaccination-related adverse events were assessed in all pwMS and HC.

Results

We found a pronounced and increasing anti-SARS-CoV-2(S)-antibody response after COVID-19 booster vaccinations in OCR-pwMS (pv2: 30.4%, pv3: 56.5%, and pv4 90.0% were antibody positive). More than one third of OCR-pwMS without detectable antibodies pv2 developed positive antibodies pv3. 23.5% of OCR-pwMS had a confirmed SARS-CoV-2 infection, of which 84.2% were symptomatic. Infection rates were comparable between OCR-pwMS and control groups. None of the pwMS had severe COVID-19. An attenuated humoral immune response was not associated with a higher risk of SARS-CoV-2 infection.

Discussion

Additional COVID-19 vaccinations can boost the humoral immune response in OCR-pwMS and improve clinical protection against COVID-19. Vaccines effectively protect even OCR-pwMS without a detectable COVID-19 specific humoral immune response, indicating compensatory, e.g., T cell-mediated immunological mechanisms.

Health Priorities of Multi-Morbid Ambulatory Patients in New York City During the COVID-19 Pandemic: A Qualitative Analysis

During the COVID-19 pandemic, adults with chronic conditions delayed or avoided seeking preventative and general medical care, leading to adverse consequences for morbidity and mortality. In order to bring patients back into care, we, in this qualitative study, sought to understand the foremost health-related needs of our multi-morbid ambulatory patients to inform future outreach interventions. Via a telephone-based survey of our high-risk patients, defined using a validated EPIC risk model for hospitalization and ED visits, we surveyed 214 participants an open-ended question, “What is your top health concern that you would like to speak with a doctor or nurse about”. We found 4 major themes: 1) primary care matters, 2) disruptions in health care, 3) COVID-19ʹs impact on physical and mental health, and 4) amplified social vulnerabilities. Our results suggest that interventions that reduce barriers to preventative services and disruptions to healthcare delivery are needed.

Multimorbidity Profiles and Infection Severity in COVID-19 Population Using Network Analysis in the Andalusian Health Population Database

Identifying the population at risk of COVID-19 infection severity is a priority for clinicians and health systems. Most studies to date have only focused on the effect of specific disorders on infection severity, without considering that patients usually present multiple chronic diseases and that these conditions tend to group together in the form of multimorbidity patterns. In this large-scale epidemiological study, including primary and hospital care information of 166,242 patients with confirmed COVID-19 infection from the Spanish region of Andalusia, we applied network analysis to identify multimorbidity profiles and analyze their impact on the risk of hospitalization and mortality. Our results showed that multimorbidity was a risk factor for COVID-19 severity and that this risk increased with the morbidity burden. Individuals with advanced cardio-metabolic profiles frequently presented the highest infection severity risk in both sexes. The pattern with the highest severity associated in men was present in almost 28.7% of those aged ≥ 80 years and included associations between cardiovascular, respiratory, and metabolic diseases; age-adjusted odds ratio (OR) 95% confidence interval (1.71 (1.44–2.02)). In women, similar patterns were also associated the most with infection severity, in 7% of 65–79-year-olds (1.44 (1.34–1.54)) and in 29% of ≥80-year-olds (1.35 (1.18–1.53)). Patients with mental health patterns also showed one of the highest risks of COVID-19 severity, especially in women. These findings strongly recommend the implementation of personalized approaches to patients with multimorbidity and SARS-CoV-2 infection, especially in the population with high morbidity burden.