Defining comorbidity: implications for understanding health and health services

Combined influence of depression symptoms and ratio of triglyceride to high-density lipoprotein cholesterol on cardiometabolic multimorbidity: Findings from the China Health and Retirement Longitudinal Study 2011-2018

BACKGROUND

Previous studies had reported depression symptoms and TG/HDLC ratio may share pathophysiological pathway. The aim was to investigate the combined effects of depression symptoms and TG/HDL-C ratio on the risk of CMM.

METHODS

This cohort study extracted data from 2011 to 2018 of CHARLS. The CMM event occurred from 2013 to 2018, defined as suffering from more than one of stroke, cardiac events, and diabetes mellitus. Cox proportional hazards regression models were used to assess the association between the baseline combined effects of depression symptoms and TG/HDL-C ratio with incidence of CMM, stroke, cardiac events, and diabetes mellitus.

RESULTS

A total of 8349 participants (3966 men and 4383 women) were included in the study, with a mean age of 58.5 years. During a 7-year follow-up survey, 370 (4.43 %) participants developed CMM. Compared to individuals with no depression symptoms and low TG/HDLC ratio, the multivariable-adjusted HRs (95%CI) for the new-onset CMM for patients with the depression symptoms alone, high TG/HDLC ratio alone, and depression symptoms and high TG/HDLC ratio were 1.37 (95 % CI = 0.95-1.98), 1.62 (95 % CI = 1.22-2.14), 1.94 (95 % CI = 1.39-2.72), respectively (P < 0.001).

LIMITATIONS

Firstly, potential confounding factors such as dietary intake and nutrition were not collected at the time of study design. Secondly, exposure to the outcome was self-reported, which may cause recall bias or misclassification. Finally, the population was aged ≥45 years, so the results cannot be generalized to all age groups.

CONCLUSION

Our findings indicated that patients with depression and high TG/HDLC ratio had a higher risk of developing CMM.

The rat as an animal model in chronic wound research: An update

The increasing global prevalence of chronic wounds underscores the growing importance of developing effective animal models for their study. This review offers a critical evaluation of the strengths and limitations of rat models frequently employed in chronic wound research and proposes potential improvements. It explores these models in the context of key comorbidities, including diabetes, venous and arterial insufficiency, pressure-induced blood flow obstruction, and infections. Additionally, the review examines important wound factors including age, sex, smoking, and the impact of anesthetic and analgesic drugs, acknowledging their substantial effects on research outcomes. A thorough understanding of these variables is crucial for refining animal models and can provide valuable insights for future research endeavors.

Towards a comprehensive approach to the management and prognosis of systemic sclerosis's patients: The role of comorbidities in the SPRING-SIR registry

OBJECTIVES

The current knowledge about the role of comorbidities in systemic sclerosis (SSc) is limited. Therefore, the aim of this study was to evaluate the prevalence of comorbidities and their impact on disease activity and prognosis in the Systemic sclerosis PRogression INvestiGation (SPRING) registry.

METHODS

SSc patients from the SPRING registry, fulfilling the ACR/EULAR 2013 classification criteria, with complete data on baseline comorbidities were enrolled. The Charlson comorbidity index (CCI) was used to quantify the overall comorbidity burden. The disease activity was calculated using the revised EUSTAR activity index (AI). The impact of SSc features on CCI, the effect of CCI on SSc disease activity and mortality were tested with multivariable regression models.

RESULTS

Among 1910 SSc patients enrolled, 67.3 % had at least one comorbidity at baseline. The most frequent comorbidities were systemic arterial hypertension (23.7 %), osteoporosis (12.9 %) and dyslipidemia (11 %). The mean value of CCI score was 2.0 ± 1.8. When patients were grouped according to increasing levels of CCI, a clear separation in the distribution of SSc-related clinical features could be observed. Among over 900 patients with available follow-up, no association between baseline CCI and changes in disease activity was observed. Conversely, the risk of death over time was independently predicted by both CCI and AI.

CONCLUSIONS

Comorbidities and disease activity independently impact on the prognosis of SSc patients. This suggests that the management of comorbidities, together with the reduction of disease activity, is fundamental to improve patient survival.

Comorbidity in primary care - causal or casual? A longitudinal observational study in family medicine

BACKGROUND

Comorbidity is increasingly important in the medical literature, with ever-increasing implications for diagnosis, treatment, prognosis, management and health care. The objective of this study is to measure casual versus causal comorbidity in primary care in three family practice populations.

METHODS

This is a longitudinal observational study using the Transition Project datasets. Transition Project family doctors in the Netherlands, Malta and Serbia recorded details of all patient contacts in an episode of care structure using electronic medical records and the International Classification of Primary Care, collecting data on all elements of the doctor-patient encounter, including diagnoses (1,178,178 in the Netherlands, 93,606 in Malta, 405,150 in Serbia), observing 158,370 patient years in the Netherlands, 43,577 in Malta, 72,673 in Serbia. Comorbidity was measured using the odds ratio of both conditions being incident or rest-prevalent in the same patient in one-year dataframes, as against not, corrected for the prior probability of such co-occurrence, between the 41 joint most prevalent (joint top 20) episode titles in the three populations. Specific associations were explored in different age groups to observe the changes in odds ratios with increasing age as a surrogate for a temporal or biological gradient.

RESULTS

The high frequency of observed comorbidity with low consistency in both clinically and statistically significant odds ratios across populations indicates more casual than causal associations. A causal relationship would be expected to be manifest more consistently across populations. Even in the minority of cases where odds ratios were consistent between countries and numerically larger, those associations were observed to weaken with increasing patient age.

CONCLUSION

After applying accepted criteria for testing the causality of associations, most observed primary care comorbidity is due to chance, likely as a result of increasing illness diversity.

TRIAL REGISTRATION

This study was performed on electronic patient record datasets made publicly available by the University of Amsterdam Department of General Practice, and did not involve any patient intervention.

Integrating multi-task and cost-sensitive learning for predicting mortality risk of chronic diseases in the elderly using real-world data

BACKGROUND AND OBJECTIVE

Real-world data encompass population diversity, enabling insights into chronic disease mortality risk among the elderly. Deep learning excels on large datasets, offering promise for real-world data. However, current models focus on single diseases, neglecting comorbidities prevalent in patients. Moreover, mortality is infrequent compared to illness, causing extreme class imbalance that impedes reliable prediction. We aim to develop a deep learning framework that accurately forecasts mortality risk from real-world data by addressing comorbidities and class imbalance.

METHODS

We integrated multi-task and cost-sensitive learning, developing an enhanced deep neural network architecture that extends multi-task learning to predict mortality risk across multiple chronic diseases. Each patient cohort with a chronic disease was assigned to a separate task, with shared lower-level parameters capturing inter-disease complexities through distinct top-level networks. Cost-sensitive functions were incorporated to ensure learning of positive class characteristics for each task and achieve accurate prediction of the risk of death from multiple chronic diseases.

RESULTS

Our study covers 15 prevalent chronic diseases and is experimented with real-world data from 482,145 patients (including 9,516 deaths) in Shenzhen, China. The proposed model is compared with six models including three machine learning models: logistic regression, XGBoost, and CatBoost, and three state-of-the-art deep learning models: 1D-CNN, TabNet, and Saint. The experimental results show that, compared with the other compared algorithms, MTL-CSDNN has better prediction results on the test set (ACC=0.99, REC=0.99, PRAUC=0.97, MCC=0.98, G-means = 0.98).

CONCLUSIONS

Our method provides valuable insights into leveraging real-world data for precise multi-disease mortality risk prediction, offering potential applications in optimizing chronic disease management, enhancing well-being, and reducing healthcare costs for the elderly population.

Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology

Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.

Interaction between Extreme Temperature Events and Fine Particulate Matter on Cardiometabolic Multimorbidity: Evidence from Four National Cohort Studies

Accumulating evidence linked extreme temperature events (ETEs) and fine particulate matter (PM2.5) to cardiometabolic multimorbidity (CMM); however, it remained unknown if and how ETEs and PM2.5 interact to trigger CMM occurrence. Merging four Chinese national cohorts with 64,140 free-CMM adults, we provided strong evidence among ETEs, PM2.5 exposure, and CMM occurrence. Performing Cox hazards regression models along with additive interaction analyses, we found that the hazards ratio (HRs) of CMM occurrence associated with heatwave and cold spell were 1.006-1.019 and 1.063-1.091, respectively. Each 10 μg/m3 increment of PM2.5 concentration was associated with 17.9% (95% confidence interval: 13.9-22.0%) increased risk of CMM. Similar adverse effects were also found among PM2.5 constituents of nitrate, organic matter, sulfate, ammonium, and black carbon. We observed a synergetic interaction of heatwave and PM2.5 pollution on CMM occurrence with relative excess risk due to the interaction of 0.999 (0.663-1.334). Our study provides novel evidence that both ETEs and PM2.5 exposure were positively associated with CMM occurrence, and the heatwave interacts synergistically with PM2.5 to trigger CMM.

Mechanisms Underlying Sex Differences in Temporomandibular Disorders and Their Comorbidity with Migraine

Sexual dimorphism in temporomandibular disorders (TMDs) and their comorbidity with migraine are important phenomena observed in clinics. TMDs are the most prevalent orofacial pain conditions with jaw joint and masseter muscle dysfunction. Migraine is the predominant headache commonly associated with TMDs. Women much more often suffer from this orofacial pain than men. However, currently, there is no gender-specific therapy for such pain conditions. Understanding the pathophysiological mechanisms behind sex differences in TMDs as well as their comorbidity with migraines is essential for developing novel approaches for gender-specific treatment of TMDs and related orofacial pain comorbidity. In this review, we summarize recent research progress regarding sex differences in TMDs, focusing on the underlying mechanisms including craniofacial anatomy, hormonal regulation, and roles of opioids, transient receptor potential channels, and endocannabinoid systems. We also discuss the mechanisms of comorbid TMDs and migraine. The information covered in this review will provide mechanistic insights into sex differences in TMDs and their comorbidity with migraine, which could aid in developing effective treatment strategies for the overlapping orofacial pain condition.

Enhanced Comprehensive Care Programme: a retrospective study of patient empanelment by generalist-led multidisciplinary teams to reduce acute care utilisation

INTRODUCTION

Singapore faces an increasingly aged population with complex multimorbidity and psychosocial impairment. This change in demographic is challenging for existing healthcare systems. Breaks in care coordination and continuity result in poor health outcomes, increased acute care utilisation and higher healthcare costs. We proposed a patient empanelment approach adapted for the Singapore context based on the University of Chicago Comprehensive Care Physician model.

METHODS

This retrospective quasi-experimental, matched-controlled observational study sought to assess the effectiveness of the Enhanced Comprehensive Care Programme (ECCP) in reducing acute care utilisation at National University Hospital, Singapore. The primary outcomes were the number of hospitalisations and emergency department (ED) visits 6 months pre- and post-enrolment in ECCP. We used propensity score matching to balance prior healthcare utilisation between the intervention and control groups.

RESULTS

Fifty-seven participants were recruited in the programme between October 2019 and April 2020. There was a reduction in the mean number of hospitalisations after intervention compared to before intervention (0.58 ± 1.03 vs. 1.90 ± 1.07, P < 0.001). There was also a reduction in the mean number of ED visits (0.77 ± 1.05 vs. 1.96 ± 1.14, P < 0.001). In the propensity-matched cohort, the mean number of hospitalisations was reduced in the intervention group (from 1.92 ± 1.07 to 0.58 ± 1.03, P < 0.001) compared to the control group (from 1.85 ± 0.99 to 1.06 ± 1.17, P = 0.04).

CONCLUSION

This observational study shows the potential benefits of ECCP healthcare redesign to reduce acute care utilisation.

Physical multimorbidity in psychiatric patients with personality disorders: Insights within the ICD-11 framework

OBJECTIVE

Recent research has revealed poor physical health among individuals with personality disorders (PDs). We aimed to compare chronic physical illnesses (CPI) and chronic physical multimorbidity (CPM) prevalence between the general population (GEP) and PD patients, and to explore the relationship between CPM and various aspects of PD, predominantly within the ICD-11 framework.

METHODS

This cross-sectional study included 126 PD patients and 126 matched controls from the GEP. Patients were evaluated for the ICD-11 PD severity and maladaptive personality domains, subjective emptiness, and reflective functioning. CPI was assessed using a standardized self-report questionnaire.

RESULTS

PD patients had a higher mean number of CPIs (2.05 vs. 1.02) and a more frequent CPM occurrence (49.2% vs. 26.2%) compared to the matched controls (p < .001). The ICD-11 PD severity (OR = 1.143, p = .007) and maladaptive domain Negative affectivity (OR = 4.845, p = .002), and poor reflective functioning (OR = 1.694, p = .007) were significant predictors of CPM, independent of sociodemographic, clinical and lifestyle factors. Negative affectivity showed the most robust effect on CPM, while smoking did not significantly mediate these relationships.

CONCLUSION

Our study found increased CPM burden in PD patients and a link between CPM and various PD aspects under the ICD-11 framework, highlighting the need for more integrated healthcare.

Extraarticular manifestations of juvenile idiopathic arthritis and their impact on health-related quality of life

OBJECTIVES

The objective of this study is to investigate extraarticular manifestations (EAMs) in patients with juvenile idiopathic arthritis (JIA) and assess their impact on health-related quality of life (HRQoL) among these patients.

METHODS

This cross-sectional analytic study was carried out on 117 patients with JIA. EAMs were identified clinically by history and examination. Sicca symptoms, peripheral neuropathy, enthesitis, and skin lesions were picked up during clinical examination. Pulmonary involvement was evaluated by high-resolution CT chest. Patients were assessed by abdominal ultrasonography to assess the size of liver and spleen. Atlantoaxial subluxation was evaluated by cervical spine x-rays. Patients were evaluated by Pediatric Quality of Life Inventory-4 (PedsQL-4) and PedsQL-3 arthritis module.

RESULTS

The median age of patients was 14 years with a median disease duration 4 years, 82.9% were females. Of the studied 117 JIA patients, 85 patients (72.6%) had at least one EAM. Persistent fatigue (51.3%) was the most prevalent EAM, followed by recurrent skin rash (16.2%), enthesitis (15.4%), recurrent fever (13.7%), and uveitis (12%). Patients with EAMs scored significantly lower in physical functioning (p = 0.001), emotional functioning (p < 0.001), social functioning (p = 0.005), and school functioning (p = 0.001). Regarding PedsQL arthritis module, patients with EAM had also significantly lower scores than did patients without EAM on the domains of pain and hurt (p < 0.001), daily activities (p = 0.008), and worry (p = 0.001).

RESULTS

EAMs are prevalent among JIA patients and have a negative impact on their HRQoL. So, early identification and treatment are highly recommended. Key Points • A large percentage of JIA patients experienced at least one extraarticular manifestation (EAM). • Persistent fatigue and recurrent skin rash are the most prevalent EAMs in JIA patients. • JIA patients with EAMs have worse scores in almost all domains of HRQoL.

Aging and "Age-Related" Diseases - What Is the Relation?

The study explores the intricate relationship between aging and the development of noncommunicable diseases [NCDs], focusing on whether these diseases are inevitable consequences of aging or primarily driven by lifestyle factors. By examining epidemiological data, particularly from hunter-gatherer societies, the study highlights that many NCDs prevalent in modern populations are rare in these societies, suggesting a significant influence of lifestyle choices. It delves into the mechanisms through which poor diet, smoking, and other lifestyle factors contribute to systemic physiological imbalances, characterized by oxidative stress, insulin resistance and hyperinsulinemia, and dysregulation of the sympathetic nervous system, the renin-angiotensin-aldosterone system, and the immune system. The interplay between this pattern and individual factors such as genetic susceptibility, biological variability, epigenetic changes and the microbiome is proposed to play a crucial role in the development of a range of age-related NCDs. Modified biomolecules such as oxysterols and advanced glycation end products also contribute to their development. Specific diseases such as benign prostatic hyperplasia, Parkinson's disease, glaucoma and osteoarthritis are analyzed to illustrate these mechanisms. The study concludes that while aging contributes to the risk of NCDs, lifestyle factors play a crucial role, offering potential avenues for prevention and intervention through healthier living practices. One possible approach could be to try to restore the physiological balance, e.g. through dietary measures [e.g. Mediterranean diet, Okinawan diet or Paleolithic diet] in conjunction with [a combination of] pharmacological interventions and other lifestyle changes.

An analysis of exogenous harmful substance exposure as risk factors for COPD and hypertension co-morbidity using PSM

Background

Some occupational and environmental exposures could increase the risk of chronic obstructive pulmonary disease (COPD) and hypertension in various work and living environments. However, the effect of exposure to multiple exogenous harmful substances on COPD and hypertension co-morbidities remains unclear.

Methods

Participants were selected from eight hospitals in five provinces in China using a multistage cluster sampling procedure. Participants' demographic, exposure, and disease information were collected through questionnaires, spirometry, and blood pressure examinations. Demographic data were used as matching factors, and 1:1 matching between the exposed and non-exposed groups was performed by employing propensity score matching (PSM) to minimize the influence on the results. A one-way chi-squared analysis and multifactorial logistic regression were used to analyze the association between the exposure to exogenous harmful substances (metals and their compound dust, inorganic mineral dust, organic chemicals, and livestock by-products) and the co-morbidity of COPD and hypertension.

Results

There were 6,610 eligible participants in the final analysis, of whom 2,045 (30.9%) were exposed to exogenous harmful substances. The prevalence of co-morbidities of COPD and hypertension (6.0%) in the exposure group was higher than their prevalence in the total population (4.6%). After PSM, exogenous harmful substance exposure was found to be a risk factor for the co-morbidity of COPD and hypertension [odds ratio (OR) = 1.347, 95% confidence interval (CI): 1.011-1.794], which was not statistically significant before PSM (OR = 1.094, 95% CI: 0.852-1.405). Meanwhile, the results of different outcomes showed that the association between hypertension and exogenous harmful substance exposure was not statistically significant (OR = 0.965, 95% CI: 0.846-1.101). Smoking (OR = 4.702, 95% CI: 3.321-6.656), history of a respiratory disease during childhood (OR = 2.830, 95% CI: 1.600-5.006), and history of respiratory symptoms (OR = 1.897, 95% CI: 1.331-2.704) were also identified as risk factors for the co-morbidity of COPD and hypertension.

Conclusion

The distribution of exogenous harmful substance exposure varies in the population, and the prevalence of co-morbidities is generally higher in susceptible populations. Exposure to exogenous harmful substances was found to be a key risk factor after adjusting for demographic confounders.

Multicentric Assessment of a Multimorbidity-Adjusted Disability Score to Stratify Depression-Related Risks Using Temporal Disease Maps: Instrument Validation Study

BACKGROUND

Comprehensive management of multimorbidity can significantly benefit from advanced health risk assessment tools that facilitate value-based interventions, allowing for the assessment and prediction of disease progression. Our study proposes a novel methodology, the Multimorbidity-Adjusted Disability Score (MADS), which integrates disease trajectory methodologies with advanced techniques for assessing interdependencies among concurrent diseases. This approach is designed to better assess the clinical burden of clusters of interrelated diseases and enhance our ability to anticipate disease progression, thereby potentially informing targeted preventive care interventions.

OBJECTIVE

This study aims to evaluate the effectiveness of the MADS in stratifying patients into clinically relevant risk groups based on their multimorbidity profiles, which accurately reflect their clinical complexity and the probabilities of developing new associated disease conditions.

METHODS

In a retrospective multicentric cohort study, we developed the MADS by analyzing disease trajectories and applying Bayesian statistics to determine disease-disease probabilities combined with well-established disability weights. We used major depressive disorder (MDD) as a primary case study for this evaluation. We stratified patients into different risk levels corresponding to different percentiles of MADS distribution. We statistically assessed the association of MADS risk strata with mortality, health care resource use, and disease progression across 1 million individuals from Spain, the United Kingdom, and Finland.

RESULTS

The results revealed significantly different distributions of the assessed outcomes across the MADS risk tiers, including mortality rates; primary care visits; specialized care outpatient consultations; visits in mental health specialized centers; emergency room visits; hospitalizations; pharmacological and nonpharmacological expenditures; and dispensation of antipsychotics, anxiolytics, sedatives, and antidepressants (P<.001 in all cases). Moreover, the results of the pairwise comparisons between adjacent risk tiers illustrate a substantial and gradual pattern of increased mortality rate, heightened health care use, increased health care expenditures, and a raised pharmacological burden as individuals progress from lower MADS risk tiers to higher-risk tiers. The analysis also revealed an augmented risk of multimorbidity progression within the high-risk groups, aligned with a higher incidence of new onsets of MDD-related diseases.

CONCLUSIONS

The MADS seems to be a promising approach for predicting health risks associated with multimorbidity. It might complement current risk assessment state-of-the-art tools by providing valuable insights for tailored epidemiological impact analyses of clusters of interrelated diseases and by accurately assessing multimorbidity progression risks. This study paves the way for innovative digital developments to support advanced health risk assessment strategies. Further validation is required to generalize its use beyond the initial case study of MDD.

Exploring the Co-Occurrence of Depressive Symptoms and Aggression among Chinese Adolescents: Patterns and Stability

Depressive symptoms and aggression frequently occur together, and this co-occurrence may result in more severe developmental problems. However, it is unclear if there are distinct patterns of co-occurrence. This study investigated the co-occurrence patterns of depressive symptoms and aggression, and examined their stability and demographic characteristics. A total of 1010 Chinese adolescents (50.6% girls; mean age at T1 = 12.54 years, SD = 0.42) participated in annual surveys over three years (2019-2021). Three different patterns of co-occurrence were found except for the normal group: depression-dominant co-occurrence (13.6%), aggression-dominant co-occurrence (3.2%), and moderate co-occurrence (6.0%) (T1). In these co-occurrence patterns, adolescents classified as aggression-dominant co-occurrence exhibited the most instability and frequent changes, while adolescents classified as depression-dominant co-occurrence exhibited the most stability. Boys or younger adolescents were more likely to exhibit the aggression-dominant co-occurrence, while girls or older adolescents were more likely to exhibit the depression-dominant co-occurrence. The findings indicate that the co-occurrence patterns observed are distinct and are dominated by aggression or depression, which implies the need for targeted intervention practices.

Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk

OBJECTIVE

To assess the effectiveness of different machine learning models in estimating the pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II diagnosis, based on the clinical risk index determined by the analysis of comorbidities.

MATERIALS AND METHODS

In this cross-sectional study, we have used data from 11,028 anonymized records of patients admitted to a high-complexity hospital in Bogota, Colombia between 2017-2019 with a primary diagnosis of Diabetes. These cases were classified according to Charlson's comorbidity index in several risk categories. The main variables analyzed in this study are hospitalization costs (which include pharmaceutical and non-pharmaceutical expenditures), age, gender, length of stay, medicines and services consumed, and comorbidities assessed by the Charlson's index. The model's dependent variable is expenditure (composed of pharmaceutical and non-pharmaceutical expenditures). Based on these variables, different machine learning models (Multivariate linear regression, Lasso model, and Neural Networks) were used to estimate the pharmaceutical and non-pharmaceutical expenditures associated with the clinical risk classification. To evaluate the performance of these models, different metrics were used: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2).

RESULTS

The results indicate that the Neural Networks model performed better in terms of accuracy in predicting pharmaceutical and non-pharmaceutical expenditures considering the clinical risk based on Charlson's comorbidity index. A deeper understanding and experimentation with Neural Networks can improve these preliminary results, therefore we can also conclude that the main variables used and those that were proposed can be used as predictors for the medical expenditures of patients with diabetes type-II.

CONCLUSIONS

With the increase of technology elements and tools, it is possible to build models that allow decision-makers in hospitals to improve the resource planning process given the accuracy obtained with the different models tested.

The impact of comorbidity on the quality of life of people who recovered from COVID-19 in Bangladesh

Objectives

COVID-19 affects the quality of life (QoL) in a reverse way after recovery, which might be multiplied by the comorbid non-communicable diseases. This study explored the relationship between comorbidities and the QoL of people who recovered from COVID-19 in Bangladesh.

Methods

The cross-sectional study was conducted among 3244 participants between June 2020 and November 2020 using a pre-tested questionnaire through over-the-phone interviews. The WHOQOL-BREF was used to explore the QoL among the study participants. A multivariable linear regression model was conducted to identify the effects of the number of comorbidities on QoL scores of all four domains.

Results

Of 3244 patients who recovered from COVID-19, over one-third (39.4%) had one or more chronic diseases, such as hypertension, diabetes mellitus, ischemic heart disease, bronchial asthma/chronic obstructive pulmonary disease, chronic kidney disease, and cancer. Around 46.85% of the participants aged above 40 years presented with one to two chronic diseases, and 16.33% had three or more chronic diseases. Among all comorbidities, the participants with cancer and chronic kidney disease were found to have relatively lower scores in all four domains than other comorbidities. The lowest QoL scores were observed in the psychological domain. Those with three or more simultaneous chronic comorbidities had the lowest QoL score in all four domains: physical, psychological, social relationship, and environmental.

Conclusions

Persons who recovered from COVID-19 with comorbidities undergo a lower QoL. Therefore, special attention is required to these vulnerable groups to ensure their smooth recuperation.

Shared comorbidity of depression, migraine, insomnia, and fibromyalgia in a population-based sample

BACKGROUND

Depression, migraine, insomnia, and fibromyalgia are reportedly comorbidities. Nevertheless, no study has evaluated the comorbidity of all four of these disorders. This study aimed to investigate the comorbidity of these four disorders.

METHODS

Cross-sectional analyses were performed using data of the Circannual Change in Headache and Sleep study, an online nationwide population-based survey. Validated questionnaires were used to diagnose the disorders and measure quality of life. The change of clinical characteristics by addition of any comorbidity was analyzed using the Jonckheere-Terpstra trend test.

RESULTS

The prevalence rates of depression, migraine, insomnia, and fibromyalgia were 7.2 %, 5.6 %, 13.3 %, and 5.8 %, respectively. Among the 3030 included participants, 494 (16.3 %), 164 (5.4 %), 40 (1.3 %), and 6 (0.2 %) had one, two, three, and four of these conditions, respectively. The number of headache days per 30 days (Jonckheere-Terpstra trend test, p = 0.011) and migraine-related disability (migraine disability assessment score, p = 0.021) increased with an increase in the number of comorbidities but not with the intensity of headache (visual analog scale, p = 0.225) among participants with migraine. The severity of insomnia (Insomnia Severity Index, p < 0.001) and fibromyalgia (fibromyalgia severity score, p = 0.002) increased with additional comorbidities; however, depression (Patient Health Questionnaire-9, p = 0.384) did not show such an increase.

LIMITATIONS

The diagnoses of conditions were based on self-reported questionnaires.

CONCLUSIONS

The findings confirmed significant comorbidity between depression, migraine, insomnia, and fibromyalgia. Health professionals should be aware of the probable comorbidity of depression, migraine, insomnia, and fibromyalgia when caring for individuals with any of these four disorders.

Clinical dimensions of people with co-occurring obsessive-compulsive and related disorders and multiple sclerosis: a scoping review protocol

INTRODUCTION

Multiple sclerosis (MS) is an immune-mediated demyelinating disease with a significant burden of neuropsychiatric sequelae. These symptoms, including depression and anxiety, are predictors of morbidity and mortality in people with MS. Despite a high prevalence of obsessive-compulsive disorder in MS, potentially shared pathophysiological mechanisms and overlap in possible treatments, no review has specifically examined the clinical dimensions of people with obsessive-compulsive and related disorders (OCRD) and MS. In this scoping review, we aim to map the available knowledge on the clinical dimensions of people with co-occurring OCRD and MS. Understanding the characteristics of this population in greater detail will inform more patient-centred care and create a framework for future studies.

METHODS AND ANALYSIS

We developed a search strategy to identify all articles that include people with co-occurring OCRD and MS. The search strategy (extending to the grey literature) was applied to MEDLINE, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, CINAHL, Web of Science and ProQuest Dissertations & Theses. Records will undergo title and abstract screening by two independent reviewers. Articles meeting inclusion criteria based on title and abstract screening will go on to full-text review by the two independent reviewers. After reaching a consensus about articles for inclusion in the final review, data will be extracted using a standardised extraction form. The extracted data will include clinical characteristics of patients such as age, gender, medication use and severity of MS, among others.

ETHICS AND DISSEMINATION

This scoping review does not require research ethics approval. Results will be shared at national and/or international conferences, in a peer-reviewed journal publication, in a plain language summary and in a webinar for the general public.

Attention deficit hyperactivity disorder in children with epilepsy: a multicenter cross-sectional analysis in China

BACKGROUND

The diagnosis and treatment of attention deficit hyperactivity disorder (ADHD) comorbid with epilepsy have been insufficiently addressed in China. We conducted a study in China to investigate the current status, diagnosis, and treatment of ADHD in children to further our understanding of ADHD comorbid with epilepsy, strengthen its management, and improve patients' quality of life.

METHODS

We carried out a multicenter cross-sectional survey of children with epilepsy across China between March 2022 and August 2022. We screened all patients for ADHD and compared various demographic and clinical factors between children with and without ADHD, including gender, age, age at epilepsy onset, duration of epilepsy, seizure types, seizure frequency, presence of epileptiform discharges, and treatment status. Our objective was to explore any possible associations between these characteristics and the prevalence of ADHD.

RESULTS

Overall, 395 epilepsy patients aged 6-18 years were enrolled. The age at seizure onset and duration of epilepsy ranged from 0.1-18 to 0.5-15 years, respectively. Focal onset seizures were observed in 212 (53.6%) patients, while 293 (76.3%) patients had epileptiform interictal electroencephalogram (EEG) abnormalities. Among the 370 patients treated with anti-seizure medications, 200 (54.1%) had monotherapy. Although 189 (47.8%) patients had ADHD, only 31 received treatment for it, with the inattentive subtype being the most common. ADHD was more common in children undergoing polytherapy compared to those on monotherapy. Additionally, poor seizure control and the presence of epileptiform interictal EEG abnormalities may be associated with a higher prevalence of ADHD.

CONCLUSIONS

While the prevalence of ADHD was higher in children with epilepsy than in normal children, the treatment rate was notably low. This highlights the need to give more importance to the diagnosis and treatment of ADHD in children with epilepsy.

Medication regimen complexity and its impact on medication adherence in patients with multimorbidity at a comprehensive specialized hospital in Ethiopia

Background

Medication regimen complexity (MRC) is suspected to hinder medication adherence in patients with multiple illnesses. Despite this, the specific impact on Ethiopian patients with multimorbidity is unclear. This study assessed MRC and its impact on medication adherence in patients with multimorbidity.

Methods

A hospital-based cross-sectional study was conducted on patients with multimorbidity who had been followed at the University of Gondar Comprehensive and Specialized Hospital (UoGCSH), Ethiopia, from May to July 2021. Medication complexity was measured using the validated Medication Regimen Complexity Index (MRCI) tool, and the Adherence in Chronic Diseases Scale (ACDS) was used to measure medication adherence. Pearson's chi-square test was used to examine associations between MRCI levels and medication adherence. Ordinal logistic regression analysis was used to determine the impact of MRC and other associated variables on medication adherence. Statistical significance was determined using the adjusted odds ratio (AOR) at p-value <0.05 and its 95% confidence range.

Results

Out of 422 eligible patients, 416 (98.6%) were included in the study. The majority of participants (57.2%) were classified as having a high MRCI score with a mean (±SD) score of 9.7 (±3.4). Nearly half of the patients (49.3%) had low medication adherence. Patients with medium (AOR = 0.43, 95% CI: 0.04, 0.72) and higher (AOR = 0.31, 95% CI: 0.07, 0.79) MRCI levels had lower odds of medication adherence. In addition, monthly income (AOR = 4.59, 95% CI: 2.14, 9.83), follow-up durations (AOR = 2.31, 95% CI: 1.09, 4.86), number of medications (AOR = 0.63, 95% CI: 0.41, 0.97), and Charlson comorbidity index (CCI) (AOR = 0.36, 95% CI: 0.16, 0.83) were significantly associated with medication adherence.

Conclusion

Medication regimen complexity in patients with multimorbidity was found to be high and negatively impacted the levels of medication adherence. Healthcare providers and other stakeholders should seek interventions aimed at simplifying drug regimen complexity and improving adherence.

Association Between Challenging Behaviour and Sleep Problems in Adults Enrolled in the Global Angelman Syndrome Registry

Angelman Syndrome (AS) is a rare genetic disorder that impacts 1:20,000 people. Challenging behaviour, such as severe injurious behaviour, aggression and frequent unprovoked episodes of laughter are a significant problem among adults with AS that adversely impacts an individual's quality of life. This study, for the first time, aims understand the characteristic of challenging behaviour, its frequency, and the factors associated with it in adults with AS. Data from participants with AS (N = 37; aged 18-46 years) registered with the Global Angelman Registry, were divided into challenging behaviour and non-challenging behaviour groups based on the presence or absence of 50% of the behaviours recorded in the registry. Descriptive statistics, chi-squared and t-test analysis were conducted to assess the impact of variables on challenging behaviour. Multiple regressions were conducted to investigate the predictors of challenging behaviour. 56% of the sample presented with challenging behaviour. Disorders of arousal, self-injury, behaviour dysregulation, repetitive behaviour, and the lack of physical therapy accounted for 59% of the variance of challenging behaviour in this population. It was found that challenging behaviour was very common in this population. A significant association was found between challenging behaviour and both sleep arousal and the lack of physical therapy. Sleep arousal and the lack of physical therapy were the key factors associated with challenging behaviour in this study. Targeted interventions are needed to decrease challenging behaviour and future research should focus on sleep interventions and increased opportunities for physical therapy.

al-BERT: a semi-supervised denoising technique for disease prediction

BACKGROUND

Medical records are a valuable source for understanding patient health conditions. Doctors often use these records to assess health without solely depending on time-consuming and complex examinations. However, these records may not always be directly relevant to a patient's current health issue. For instance, information about common colds may not be relevant to a more specific health condition. While experienced doctors can effectively navigate through unnecessary details in medical records, this excess information presents a challenge for machine learning models in predicting diseases electronically. To address this, we have developed 'al-BERT', a new disease prediction model that leverages the BERT framework. This model is designed to identify crucial information from medical records and use it to predict diseases. 'al-BERT' operates on the principle that the structure of sentences in diagnostic records is similar to regular linguistic patterns. However, just as stuttering in speech can introduce 'noise' or irrelevant information, similar issues can arise in written records, complicating model training. To overcome this, 'al-BERT' incorporates a semi-supervised layer that filters out irrelevant data from patient visitation records. This process aims to refine the data, resulting in more reliable indicators for disease correlations and enhancing the model's predictive accuracy and utility in medical diagnostics.

METHOD

To discern noise diseases within patient records, especially those resembling influenza-like illnesses, our approach employs a customized semi-supervised learning algorithm equipped with a focused attention mechanism. This mechanism is specifically calibrated to enhance the model's sensitivity to chronic conditions while concurrently distilling salient features from patient records, thereby augmenting the predictive accuracy and utility of the model in clinical settings. We evaluate the performance of al-BERT using real-world health insurance data provided by Taiwan's National Health Insurance.

RESULT

In our study, we evaluated our model against two others: one based on BERT that uses complete disease records, and another variant that includes extra filtering techniques. Our findings show that models incorporating filtering mechanisms typically perform better than those using the entire, unfiltered dataset. Our approach resulted in improved outcomes across several key measures: AUC-ROC (an indicator of a model's ability to distinguish between classes), precision (the accuracy of positive predictions), recall (the model's ability to find all relevant cases), and overall accuracy. Most notably, our model showed a 15% improvement in recall compared to the current best-performing method in the field of disease prediction.

CONCLUSION

The conducted ablation study affirms the advantages of our attention mechanism and underscores the crucial role of the selection module within al-BERT.

Age-adjusted Charlson comorbidity index is associated with the risk of osteoporosis in older fall-prone men: a retrospective cohort study

BACKGROUND

There is growing evidence linking the age-adjusted Charlson comorbidity index (aCCI), an assessment tool for multimorbidity, to fragility fracture and fracture-related postoperative complications. However, the role of multimorbidity in osteoporosis has not yet been thoroughly evaluated. We aimed to investigate the association between aCCI and the risk of osteoporosis in older adults at moderate to high risk of falling.

METHODS

A total of 947 men were included from January 2015 to August 2022 in a hospital in Beijing, China. The aCCI was calculated by counting age and each comorbidity according to their weighted scores, and the participants were stratified into two groups by aCCI: low (aCCI < 5), and high (aCCI ≥5). The Kaplan Meier method was used to assess the cumulative incidence of osteoporosis by different levels of aCCI. The Cox proportional hazards regression model was used to estimate the association of aCCI with the risk of osteoporosis. Receiver operating characteristic (ROC) curve was adapted to assess the performance for aCCI in osteoporosis screening.

RESULTS

At baseline, the mean age of all patients was 75.7 years, the mean BMI was 24.8 kg/m2, and 531 (56.1%) patients had high aCCI while 416 (43.9%) were having low aCCI. During a median follow-up of 6.6 years, 296 participants developed osteoporosis. Kaplan-Meier survival curves showed that participants with high aCCI had significantly higher cumulative incidence of osteoporosis compared with those had low aCCI (log-rank test: P < 0.001). When aCCI was examined as a continuous variable, the multivariable-adjusted model showed that the osteoporosis risk increased by 12.1% (HR = 1.121, 95% CI 1.041-1.206, P = 0.002) as aCCI increased by one unit. When aCCI was changed to a categorical variable, the multivariable-adjusted hazard ratios associated with different levels of aCCI [low (reference group) and high] were 1.00 and 1.557 (95% CI 1.223-1.983) for osteoporosis (P <  0.001), respectively. The aCCI (cutoff ≥5) revealed an area under ROC curve (AUC) of 0.566 (95%CI 0.527-0.605, P = 0.001) in identifying osteoporosis in older fall-prone men, with sensitivity of 64.9% and specificity of 47.9%.

CONCLUSIONS

The current study indicated an association of higher aCCI with an increased risk of osteoporosis among older fall-prone men, supporting the possibility of aCCI as a marker of long-term skeletal-related adverse clinical outcomes.

hART: Deep learning-informed lifespan heart failure risk trajectories

BACKGROUND

Heart failure (HF) results in persistent risk and long-term comorbidities. This is particularly true for patients with lifelong HF sequelae of cardiovascular disease such as patients with congenital heart disease (CHD).

PURPOSE

We developed hART (heart failure Attentive Risk Trajectory), a deep-learning model to predict HF trajectories in CHD patients.

METHODS

hART is designed to capture the contextual relationships between medical events within a patient's history. It is trained to predict future HF risk by using the masked self-attention mechanism that forces it to focus only on the most relevant segments of the past medical events.

RESULTS

To demonstrate the utility of hART, we used a large cohort containing healthcare administrative data from the Quebec CHD database (137,493 patients, 35-year follow-up). hART achieves an area under the precision-recall of 28% for HF risk prediction, which is 33% improvement over existing methods. Patients with severe CHD lesion showed a consistently elevated predicted HF risks throughout their lifespan, and patients with genetic syndromes exhibited elevated HF risks until the age of 50. The impact of the birth condition decreases on long-term HF risk. The timing of interventions such as arrhythmia surgery had varying impacts on the lifespan HF risk among the individuals. Arrhythmic surgery performed at a younger age had minimal long-term effects on HF risk, while surgeries during adulthood had a significant lasting impact.

CONCLUSION

Together, we show that hART can detect meaningful lifelong HF risk in CHD patients by capturing both long and short-range dependencies in their past medical events.

Noninvasive Risk Prediction Models for Heart Failure Using Proportional Jaccard Indices and Comorbidity Patterns

Background

In the post-coronavirus disease 2019 (COVID-19) era, remote diagnosis and precision preventive medicine have emerged as pivotal clinical medicine applications. This study aims to develop a digital health-monitoring tool that utilizes electronic medical records (EMRs) as the foundation for performing a non-random correlation analysis among different comorbidity patterns for heart failure (HF).

Methods

Novel similarity indices, including proportional Jaccard index (PJI), multiplication of the odds ratio proportional Jaccard index (OPJI), and alpha proportional Jaccard index (APJI), provide a fundamental framework for constructing machine learning models to predict the risk conditions associated with HF.

Results

Our models were constructed for different age groups and sexes and yielded accurate predictions of high-risk HF across demographics. The results indicated that the optimal prediction model achieved a notable accuracy of 82.1% and an area under the curve (AUC) of 0.878.

Conclusions

Our noninvasive HF risk prediction system is based on historical EMRs and provides a practical approach. The proposed indices provided simple and straightforward comparative indicators of comorbidity pattern matching within individual EMRs. All source codes developed for our noninvasive prediction models can be retrieved from GitHub.

The role of benzodiazepines in common conditions: a narrative review focusing on lormetazepam

This review aimed to examine the place of benzodiazepines, specifically lormetazepam, in the treatment of insomnia, including during pregnancy or in patients with psychodermatoses. PubMed was searched for the term "lormetazepam" in association with MeSH terms encompassing anxiety, insomnia/sleep disorders, pregnancy/gestation, and psychodermatoses/skin disorders. English-language articles up to 31 July 2022 were identified. Ad hoc searches for relevant literature were performed at later stages of review development. Multiple randomized, placebo-controlled studies have demonstrated that lormetazepam dose-dependently increases total sleep time, decreases wakefulness over a dosing range of 0.5-2.0 mg, and improves subjective assessments of sleep quality. Lormetazepam is as effective as other benzodiazepines in improving sleep duration and quality, but is better tolerated than the long-acting agents with minimal next-day effects. Benzodiazepines can be used as short-term monotherapy at the lowest effective dose during the second or third trimesters of pregnancy; lormetazepam is also a reasonable choice due to its limited transplacental passage. Insomnia associated with skin disorders or pregnancy can be managed by effective symptom control (especially itching), sleep hygiene, treatment of anxiety/depression, and a short course of hypnotics.

Determinants associated with activation in patients with oral anticoagulant treatment: A cross-sectional study

AIMS

The terms "patient activation" and "patient empowerment" are used to describe the extent to which individuals are able to manage their own healthcare. Health outcomes and quality of life improve in patients who are more active in health care. The aim of this study was to identify sociodemographic, clinical, sociological and psychological determinants associated with activation in a group of chronically anticoagulated patients.

METHODS

Cross-sectional study of patients treated with oral antivitamin-k drugs attended at a specialized outpatient anticoagulant unit between November 2021 and June 2022. The main dependent variable was the level of patient activation according to the 13-item Patient Activation Measure (PAM-13). Simple and multiple linear regression models were conducted to identify the determinants associated with PAM-13 score.

RESULTS

A total of 137 patients who met all the inclusion criteria were recruited for the study. The mean age was 59.6 years (SD 13.8; range 22-86) and 60.6% were male. Sixty per cent presented a level IV of activation according to the PAM-13 scale. Mean patient activation score was 73.9 (SD 15.4). The factors independently associated with significantly lower activation were: emergency department visits in the past 12 months, intermediate social risk, anxiety symptoms, stress symptoms and low self-efficacy.

CONCLUSIONS

Five determinants were found to be associated with activation. Knowing the factors that modify the level of activation can help to identify subgroups of chronic anticoagulated patients who are less likely to engage in self-management and are therefore candidates for tailored educational interventions.

Multimorbidity and the Etiology of Schizophrenia

PURPOSE OF REVIEW

A global study of multimorbidity in schizophrenia, especially of the association with physical conditions, might offer much needed etiological insights.

RECENT FINDINGS

Our review suggests that life-style factors and medication related to schizophrenia are only part of the explanation of the increase in risk for cardiovascular, metabolic, pulmonary disorders, and some cancers. Positive associations with autoimmune disorders (with the exception of rheumatoid arthritis) and epilepsy are promising avenues of research but to date have not been fully exploited. The same holds for the negative comorbidity seen for rheumatoid arthritis and some cancers (e.g., prostate). As a whole, our review suggests that most of the explored conditions have a different prevalence in schizophrenia than in the general population. Several hypotheses emerged from this review such as the role of immune and genetic factors, of sex hormones, and of more general variability factors.

Tools used to measure the impact of comorbidities on surgical outcomes in children with complex chronic conditions: A scoping review

AIM

To identify and describe assessment tools used to measure the impact of comorbidities on postoperative outcomes in children with complex chronic conditions (CCC).

METHOD

This was a scoping review using five electronic databases. The search was conducted in March 2022 by a medical librarian. There were no date or language restrictions. Included studies were full-text articles published in peer-reviewed journals that described a tool used to measure the impact of comorbidities in children with CCC to assess postoperative outcomes. A standardized data charting tool was used.

RESULTS

A total of 2157 articles were retrieved. Five studies reporting on six comorbidity measures met inclusion criteria. All were cohort studies and were secondary analyses of data from an administrative database (n = 4) or a patient registry (n = 1). Sample sizes ranged from 645 to 25 747 participants. One paper described the assessment of reliability. Only one form of validity - predictive validity - was assessed in three papers for five measures.

INTERPRETATION

Findings from this scoping review revealed a paucity of comorbidity assessment tools validated for use with children with CCC; significant conceptual and measurement challenges exist in the current scientific literature.

A mouse model of chronic primary pain that integrates clinically relevant genetic vulnerability, stress, and minor injury

Chronic primary pain conditions (CPPCs) affect over 100 million Americans, predominantly women. They remain ineffectively treated, in large part because of a lack of valid animal models with translational relevance. Here, we characterized a CPPC mouse model that integrated clinically relevant genetic (catechol-O-methyltransferase; COMT knockdown) and environmental (stress and injury) factors. Compared with wild-type mice, Comt+/- mice undergoing repeated swim stress and molar extraction surgery intervention exhibited pronounced multisite body pain and depressive-like behavior lasting >3 months. Comt+/- mice undergoing the intervention also exhibited enhanced activity of primary afferent nociceptors innervating hindpaw and low back sites and increased plasma concentrations of norepinephrine and pro-inflammatory cytokines interleukin-6 (IL-6) and IL-17A. The pain and depressive-like behavior were of greater magnitude and longer duration (≥12 months) in females versus males. Furthermore, increases in anxiety-like behavior and IL-6 were female-specific. The effect of COMT genotype × stress interactions on pain, IL-6, and IL-17A was validated in a cohort of 549 patients with CPPCs, demonstrating clinical relevance. Last, we assessed the predictive validity of the model for analgesic screening and found that it successfully predicted the lack of efficacy of minocycline and the CB2 agonist GW842166X, which were effective in spared nerve injury and complete Freund's adjuvant models, respectively, but failed in clinical trials. Yet, pain in the CPPC model was alleviated by the beta-3 adrenergic antagonist SR59230A. Thus, the CPPC mouse model reliably recapitulates clinically and biologically relevant features of CPPCs and may be implemented to test underlying mechanisms and find new therapeutics.

The burden of diabetes and hypertension on healthy life expectancy in Bangladesh

Diabetes and hypertension are among the leading causes of death in Bangladesh. This study examined hypertension, diabetes, and either or both, free life expectancy, to measure the effect of the diseases on the overall health of individuals in Bangladesh with regional variations. We utilized data from Bangladesh Sample Vital Statistics 2018 for mortality and Bangladesh Demographic and Health Survey 2017-2018 for diabetes and hypertension. The Sullivan method was employed to estimate age-specific hypertension and diabetes-free life expectancy. Altogether, 10.3% of the people aged 18-19 years lived with either diabetes or hypertension. The hypertension-free life expectancy was 40.4 years, and the diabetes-free life expectancy was 53.2 years for those aged 15-19. Overall, individuals would expect to spend 38.7% of their lives with either of the diseases. Females suffered more from hypertension and males from diabetes. Still, females suffered more from the aggregate of both. Rural people had more diabetes and hypertension-free life expectancy than those of urban. Individuals of Mymensingh had the highest life expectancy free of both diseases compared to other divisions of Bangladesh. Diabetes and hypertension affect a considerable proportion of the life of the population in Bangladesh. Policy actions are needed to guide the prevention, diagnosis, and treatment of both diseases, specifically focusing on women and urban populations. Widespread health-enhancing actions need to be taken to diminish the effect of these two diseases in Bangladesh.

A Shared Multivariate Brain-Behavior Relationship in a Transdiagnostic Sample of Adolescents

BACKGROUND

Internalizing and externalizing psychopathology typically present in early childhood and can have negative implications on general functioning and quality of life. Prior work has linked increased psychopathology symptoms with altered brain structure. Multivariate analysis such as partial least squares correlation can help identify patterns of covariation between brain regions and psychopathology symptoms. This study examined the relationship between gray matter volume (GMV) and psychopathology symptoms in adolescents with various psychiatric diagnoses.

METHODS

Structural magnetic resonance imaging data were collected from 490 participants with various internalizing and externalizing diagnoses (197 female/293 male; age = 14.68 ± 2.35 years; IQ = 104.05 ± 13.11). Cortical and subcortical volumes were parcellated using the Desikan-Killiany atlas. Partial least squares correlation was used to identify multivariate linear relationships between GMV and the Strength and Difficulties Questionnaire difficulties domains (emotional, peer, conduct, and hyperactivity issues). Resampling approaches were used to determine significance (permutation test), stability (bootstrap resampling), and reproducibility (split-half resampling) of identified relationships.

RESULTS

We found a significant, stable, and largely reproducible dimension that linked lower Strength and Difficulties Questionnaire scores (less impairment) across all difficulties domains with greater widespread GMV (singular value = 1.17, accounts for 87.1% of the covariance; p < .001). This dimension emphasized the relationship between lower conduct problems and greater GMV in frontotemporal regions.

CONCLUSIONS

Our results indicate that the most significant and stable brain-behavior relationship in a transdiagnostic sample is a domain-general relationship, linking lower psychopathology symptom scores to greater global GMV. This finding suggests that a shared brain-behavior relationship may be present across adolescents with and without clinically significant psychopathology symptoms.

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

BACKGROUND

Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change.

OBJECTIVE

The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months.

METHODS

Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered.

RESULTS

Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants' outcomes (eg, reduce symptom exacerbation and increase physical activity).

CONCLUSIONS

The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.

How to incorporate chronic health conditions in oncologic decision-making and care for older patients with cancer? A survey among healthcare professionals

PURPOSE

A substantial proportion of patients with cancer are older and experience multimorbidity. As the population is ageing, the management of older patients with multimorbidity including cancer will represent a significant challenge to current clinical practice.

METHODS

This study aimed to (1) identify which chronic health conditions may cause change in oncologic decision-making and care in older patients and (2) provide guidance on how to incorporate these in decision-making and care provision of older patients with cancer. Based on a scoping literature review, an initial list of prevalent morbidities was developed. A subsequent survey among healthcare providers involved in the care for older patients with cancer assessed which chronic health conditions were relevant and why.

RESULTS

A list of 53 chronic health conditions was developed, of which 34 were considered likely or very likely to influence decision-making or care according to the 39 healthcare professionals who responded. These conditions were further categorized into five patient profiles. From these conditions, five patient profiles were developed, namely, (1) a somatic profile consisting of cardiovascular, metabolic, and pulmonary disease, (2) a functional profile, including conditions that cause disability, dependency or a high caregiver burden, (3) a psychosocial profile, including cognitive impairment, (4) a nutritional profile also including digestive system diseases, and finally, (5) a concurrent cancer profile. All profiles were considered likely to impact decision-making with differences between treatment modalities. The impact on the care trajectory was generally considered less significant, except for patients with care dependency and psychosocial health problems.

CONCLUSIONS

Chronic health conditions have various ways of influencing oncologic decision-making and the care trajectory in older adults with cancer. Understanding why specific chronic health conditions may impact the oncologic care trajectory can aid clinicians in the management of older patients with multimorbidity, including cancer.

Co-occurrence of habit-forming risk behaviors and their socio-demographic, health status and lifestyle determinants: a population-based cross-sectional study

BACKGROUND

Although habit-forming risk behaviors frequently co-occur, determinants of concurrent risk behaviors have rarely been investigated. The aim of the present study was to investigate socio-demographic, health status, and lifestyle determinants of single versus concurrent risk behaviors in general-population adults.

METHODS

We analyzed data from 32,622 participants (74.5% female; mean age = 57.9 ± 14.2 years) of the NutriNet-Santé cohort who completed the Alcohol Use Disorders Identification Test, the 12-item Cigarette Dependence Scale, the modified Yale Food Addiction Scale 2.0, and the Internet Addiction Test in 2021-2022. Using established cutoffs, participants were first split into 2 groups (presence versus absence) for each risk variable (alcohol use disorders, nicotine dependence, food addiction, Internet addiction) and were then divided into 3 groups (no risk behavior, 1 risk behavior (reference), and ≥ 2 risk behaviors). The association between socio-demographic, health status, and lifestyle exposures and individual/concurrent risk behaviors were investigated with polytomous logistic regression.

RESULTS

Younger age (Odds Ratio (OR) = 2.04; 95% Confidence Interval (CI: 1.62-2.56), current financial difficulties (OR = 1.29; CI: 1.08-1.54), self-perceived poor health (OR = 1.70; CI: 1.32-2.20), overall poor dietary quality (OR = 2.88; CI: 2.06-4.02), being underweight (OR = 1.46; CI: 1.05-2.04), having obesity (OR = 1.62; CI: 1.31-1.99), lack of affection during childhood (OR = 1.41; CI: 1.18-1.69), and a lifetime prevalence or medication use for a mental disorder (OR = 1.46; CI: 1.24-1.73) were positively associated with having ≥ 2 versus 1 risk behavior (all p < 0.05). The comparison of none versus 1 risk behavior revealed the same determinants in addition to having a higher education, being physically active at work, and being overweight.

CONCLUSIONS

We investigated determinants of concurrent habit-forming risk behaviors among adults in a large, population-based study. The findings could serve as impetus for future research in this domain and ultimately help guide addiction prevention efforts.

Pain, comorbidities, and clinical decision-making: conceptualization, development, and pilot testing of the Pain in Aging, Educational Assessment of Need instrument

Introduction

Pain is highly prevalent in older adults and often contextualized by multiple clinical conditions (pain comorbidities). Pain comorbidities increase with age and this makes clinical decisions more complex. To address gaps in clinical training and geriatric pain management, we established the Pain in Aging-Educational Assessment of Need (PAEAN) project to appraise the impacts of medical and mental health conditions on clinical decision-making regarding older adults with pain. We here report development and pilot testing of the PAEAN survey instrument to assess clinician perspectives.

Methods

Mixed-methods approaches were used. Scoping review methodology was applied to appraise both research literature and selected Medicare-based data. A geographically and professionally diverse interprofessional advisory panel of experts in pain research, medical education, and geriatrics was formed to advise development of the list of pain comorbidities potentially impacting healthcare professional clinical decision-making. A survey instrument was developed, and pilot tested by diverse licensed healthcare practitioners from 2 institutions. Respondents were asked to rate agreement regarding clinical decision-making impact using a 5-point Likert scale. Items were scored for percent agreement.

Results

Scoping reviews indicated that pain conditions and comorbidities are prevalent in older adults but not universally recognized. We found no research literature directly guiding pain educators in designing pain education modules that mirror older adult clinical complexity. The interprofessional advisory panel identified 26 common clinical conditions for inclusion in the pilot PAEAN instrument. Conditions fell into three main categories: "major medical", i.e., cardio-vascular-pulmonary; metabolic; and neuropsychiatric/age-related. The instrument was pilot tested by surveying clinically active healthcare providers, e.g., physicians, nurse practitioners, who all responded completely. Median survey completion time was less than 3 min.

Conclusion

This study, developing and pilot testing our "Pain in Aging-Educational Assessment of Need" (PAEAN) instrument, suggests that 1) many clinical conditions impact pain clinical decision-making, and 2) surveying healthcare practitioners about the impact of pain comorbidities on clinical decision-making for older adults is highly feasible. Given the challenges intrinsic to safe and effective clinical care of older adults with pain, and attendant risks, together with the paucity of existing relevant work, much more education and research are needed.

COVID-19 and spinal cord injury: clinical presentation, clinical course, and clinical outcomes of people hospitalised

STUDY DESIGN

Retrospective study OBJECTIVES: To describe the presenting symptoms/signs, clinical course and outcomes in hospitalised people with spinal cord injury (SCI) and symptomatic COVID-19 infections.

SETTING

One university hospital and two SCI centres in Switzerland.

METHODS

Descriptive analysis of symptoms/signs, clinical course and outcomes of people with SCI with symptomatic COVID-19 infections and need for hospitalisation.

RESULTS

Twenty-two people with SCI were included, 15 (68%) were male, median age 64.5 years (interquartile range, IQR, 52-73 years). Nine (41%) had tetraplegia, and eight (36%) were classified with motor-complete lesions. Frequent clinical symptoms were fever (59%), coughing (54%), fatigue (50%), and dyspnoea (27%). Most frequent complications were bacterial pulmonary superinfection (18%), and acute respiratory distress syndrome (18%). Fifteen persons (68%) needed oxygen therapy during the course of hospitalisation, and 7 (32%) people were ventilated. Median length of stay (LOS) was 23 days (IQR 15-35), varying by age for people under 60 years with a median LOS of 9 days (IQR 8-27), and for those older than 60 years with a median of 34 days (IQR 17-39), respectively. In total, 3 persons (14%) died during hospitalisation, all older with paraplegia.

CONCLUSIONS

Typical symptoms like fever and coughing were not present in all people. People with tetraplegia did not demonstrate worse outcomes, on the contrary, they had shorter LOS, no difference in ventilation needs, and no higher mortality compared to people with paraplegia. Older people showed longer LOS. This study recommends close supervision of the SCI population to detect early signs and symptoms of COVID-19 infection.

Understanding the conditions included in data-driven patterns of multimorbidity: a scoping review

BACKGROUND

Despite the growing utilization of data-driven methods to investigate multimorbidity patterns, there is currently no consensus or guidance on the conditions to include when identifying patterns. This scoping review aims to systematically examine the nature of conditions included in existing studies using data-driven techniques.

METHODS

A comprehensive search of three electronic databases (MEDLINE, Web of Science and Scopus) was conducted to identify relevant publications from inception to 28 February 2022 using predefined search terms and inclusion/exclusion criteria. The reference lists and citations of relevant papers were also searched.

RESULTS

Among 7326 search results, 5444 relevant articles were identified. After screening against the eligibility criteria, 60 articles were included in the review. Half of the reviewed studies reported selection criteria for conditions, with prevalence in the population of interest being the most common criterion (40%). Most studies included at least one neurological [59 (98.3%)], musculoskeletal [58 (96.7%)], respiratory [57 (95.0%)] or mental health [56 (93.3%)] condition. In contrast, only a small proportion of studies included skin [17 (28.3%)], infections [14 (23.3%)] or autoimmune conditions [10 (16.7%)]. Nine conditions (hypertension, diabetes, cancer, arthritis, COPD, asthma, depression, stroke and osteoporosis) were included by more than half of the studies.

CONCLUSIONS

This review highlights the considerable heterogeneity among the conditions included in analyses of multimorbidity patterns. Researchers should provide a clear rationale for the selection of conditions to facilitate comparisons across studies and ensure reproducibility, as well as consider selecting a diverse range of conditions to capture the complexity of multimorbidity.

Cerebral Perfusion Unveiled: A Comprehensive Review of Blood Pressure Management in Neurosurgical and Endovascular Aneurysm Interventions

This comprehensive review delves into the intricate dynamics of cerebral perfusion and blood pressure management within the context of neurosurgical and endovascular aneurysm interventions. The review highlights the critical role of maintaining a delicate hemodynamic balance, given the brain's susceptibility to fluctuations in blood pressure. Emphasizing the regulatory mechanisms of cerebral perfusion, particularly autoregulation, the study advocates for a nuanced and personalized approach to blood pressure control. Key findings underscore the significance of adhering to tailored blood pressure targets to mitigate the risks of ischemic and hemorrhagic complications in both neurosurgical and endovascular procedures. The implications for clinical practice are profound, calling for heightened awareness and precision in hemodynamic management. The review concludes with recommendations for future research, urging exploration into optimal blood pressure targets, advancements in monitoring technologies, investigations into long-term outcomes, and the development of personalized approaches. By consolidating current knowledge and charting a path for future investigations, this review aims to contribute to the continual enhancement of patient outcomes in the dynamic field of neurovascular interventions.

Spousal multimorbidity and depressive symptoms among older Indian couples: Do one's own health status and sex matter?

With the aging population, increases in non-communicable diseases that require chronic management pose a substantial economic and social burden to individuals with multimorbid conditions and their spousal caregivers. However, little is known about the crossover effect of spousal multimorbidity on mental health outcomes in the context of low- and middle-income countries, and whether it depends on one's own health status and sex. We examined the association between spousal multimorbidity and depressive symptoms using data on 6,158 older couples (12,316 individuals aged 60 years or above) from the Longitudinal Aging Study in India (LASI) 2017-18. Overall, 23.4% of the sample were multimorbid and 27.0% reported having depressive symptoms in the past week. Multivariable logistic regression models showed that spousal multimorbidity was associated with depressive symptoms, even after accounting for one's own multimorbidity status (OR: 1.23; 95% CI: 1.06-1.44). However, this association varied by sex. Among males, their own multimorbidity status was associated with 60% higher odds of having depressive symptoms (OR: 1.60; 95% CI: 1.28-2.00), but spousal multimorbidity was not. Furthermore, for males, the association between spousal multimorbidity and depressive symptoms was contingent upon the presence of their own multimorbidity. Among females, spousal multimorbidity was significantly associated with depressive symptoms, regardless of their own multimorbidity status. Our findings indicate that interventions to promote healthy aging should expand the formal caregiving system and consider family-based approaches to minimize the crossover health consequences of chronic morbidity in conjugal relationships, especially for females.

Population-Based Trends in Complexity of Hospital Inpatients

Importance

Clinical experience suggests that hospital inpatients have become more complex over time, but few studies have evaluated this impression.

Objective

To assess whether there has been an increase in measures of hospital inpatient complexity over a 15-year period.

Design, Setting and Participants

This cohort study used population-based administrative health data from nonelective hospitalizations from April 1, 2002, to January 31, 2017, to describe trends in the complexity of inpatients in British Columbia, Canada. Hospitalizations were included for individuals 18 years and older and for which the most responsible diagnosis did not correspond to pregnancy, childbirth, the puerperal period, or the perinatal period. Data analysis was performed from July to November 2023.

Exposure

The passage of time (15-year study interval).

Main Outcomes and Measures

Measures of complexity included patient characteristics at the time of admission (eg, advanced age, multimorbidity, polypharmacy, recent hospitalization), features of the index hospitalization (eg, admission via the emergency department, multiple acute medical problems, use of intensive care, prolonged length of stay, in-hospital adverse events, in-hospital death), and 30-day outcomes after hospital discharge (eg, unplanned readmission, all-cause mortality). Logistic regression was used to estimate the relative change in each measure of complexity over the entire 15-year study interval.

Results

The final study cohort included 3 367 463 nonelective acute care hospital admissions occurring among 1 272 444 unique individuals (median [IQR] age, 66 [48-79] years; 49.1% female and 50.8% male individuals). Relative to the beginning of the study interval, inpatients at the end of the study interval were more likely to have been admitted via the emergency department (odds ratio [OR], 2.74; 95% CI, 2.71-2.77), to have multimorbidity (OR, 1.50; 95% CI, 1.47-1.53) and polypharmacy (OR, 1.82; 95% CI, 1.78-1.85) at presentation, to receive treatment for 5 or more acute medical issues (OR, 2.06; 95% CI, 2.02-2.09), and to experience an in-hospital adverse event (OR, 1.20; 95% CI, 1.19-1.22). The likelihood of an intensive care unit stay and of in-hospital death declined over the study interval (OR, 0.96; 95% CI, 0.95-0.97, and OR, 0.81; 95% CI, 0.80-0.83, respectively), but the risks of unplanned readmission and death in the 30 days after discharge increased (OR, 1.14; 95% CI, 1.12-1.16, and OR, 1.28; 95% CI, 1.25-1.31, respectively).

Conclusions and Relevance

By most measures, hospital inpatients have become more complex over time. Health system planning should account for these trends.

Learning prevalent patterns of co-morbidities in multichronic patients using population-based healthcare data

The prevalence of longstanding chronic diseases has increased worldwide, along with the average age of the population. As a result, an increasing number of people is affected by two or more chronic conditions simultaneously, and healthcare systems are facing the challenge of treating multimorbid patients effectively. Current therapeutic strategies are suited to manage each chronic condition separately, without considering the whole clinical condition of the patient. This approach may lead to suboptimal clinical outcomes and system inefficiencies (e.g. redundant diagnostic tests and inadequate drug prescriptions). We develop a novel methodology based on the joint implementation of data reduction and clustering algorithms to identify patterns of chronic diseases that are likely to co-occur in multichronic patients. We analyse data from a large adult population of multichronic patients living in Tuscany (Italy) in 2019 which was stratified by sex and age classes. Results demonstrate that (i) cardio-metabolic, endocrine, and neuro-degenerative diseases represent a stable pattern of multimorbidity, and (ii) disease prevalence and clustering vary across ages and between women and men. Identifying the most common multichronic profiles can help tailor medical protocols to patients' needs and reduce costs. Furthermore, analysing temporal patterns of disease can refine risk predictions for evolutive chronic conditions.

Factors Associated with Lifestyle Behaviors among University Students-A Cross-Sectional Study

Lifestyle behaviors are daily habits influenced by social and environmental factors. This study examined lifestyle behaviors and their associations with sociodemographics, comorbidities, and pain in Saudi university students during the academic year 2021 and 2022. All students received the study invitation via university emails to complete an online questionnaire. The questionnaire included four sections (sociodemographics, health-related information, desired health promotion activities, and a lifestyle behavior assessment) via Health-Promoting Lifestyle Profile II (HPLP-II). The associations between study variables were assessed using Pearson's correlation and multiple linear regression. The study questionnaire was completed by 1112 students. No correlation was found between sociodemographics and lifestyle-behavior-related factors except for students in the College of Science who appeared to have good lifestyle behaviors (an increase in HPLP II total scores of 3.69). Students with mental health issues have poorer lifestyle behaviors and spend more time sitting (p < 0.00). Students without disabilities have lower scores in health responsibility, physical activity, nutrition, and stress management, while auditory disability specifically lowers health responsibility (p < 0.00). Pain was not associated with any assessed lifestyle behaviors. This study identified several significant correlations and differences between variables such as age, sedentary behavior, sleep duration, disability status, college major, and lifestyle behaviors among PNU students. These findings provide insights into the factors that influence students' health-promoting behaviors and can help guide interventions for promoting healthier lifestyles on campus. Targeted health promotion strategies at an early age could help in decreasing overall noncommunicable disease incidents later in life. The study results should be interpreted taking into consideration that the collected data were cross-sectional and self-reported. In conclusion, the findings of this study clearly demonstrate the need for specific lifestyle and health-promoting programs that are directed toward university students.

Role of TNF-α in the Pathogenesis of Migraine

Background

Neurogenic neuroinflammation has a wide role in migraine pathogenesis including the transition from episodic migraine to chronic one. The seed molecule of neurogenic neuroinflammation, i.e., the TNF-α proinflammatory molecule, has gathered a lot of attention. This pleiotropic cytokine is a classical component of inflammatory soup, secreted by the microglial cell, and promotes a wide range of inflammatory reactions.

Aim

In this review, we aimed to provide a culminating and comprehending glimpse into the TNF-α in association with the migraine.

Method

A systematic literature survey method with a mixture of keywords was utilized to grasp the different elements that represent the association between TNF-α and migraine. Discussion. Highlighted the probable involvement of the TNF-α with migraine, the complexity of the matter such as activation of NF-KB signaling cascade, autoactivation, sensitization, and increased likelihood of transition cannot be neglected. Being TNF-α as a core node, it becomes the factor for linking diseases such as chronic inflammatory disorders, including COVID-19, and also interaction with other genes to develop severe conditions.

Conclusion

To this end, TNF-α plays a critical role in chronification, and inhibiting its signaling would likely be a crucial strategy for migraine therapy.

Proton Pump Inhibitors and Cognitive Health: Review on Unraveling the Dementia Connection and Co-morbid Risks

Dementia, an international health issue distinguished by the impairment of daily functioning due to cognitive decline, currently affects more than 55 million people worldwide, with the majority residing in low-income and middle-income countries. Globally, dementia entails significant economic burdens in 2019, amounting to a cost of 1.3 trillion US dollars. Informal caregivers devote considerable hours to providing care for those affected. Dementia imposes a greater caregiving and disability-adjusted life-year burden on women. A recent study has established a correlation between prolonged Proton Pump Inhibitor (PPI) usage and dementia, in addition to other neurodegenerative conditions. PPIs are frequently prescribed to treat peptic ulcers and GERD (gastroesophageal reflux disease) by decreasing stomach acid secretion. They alleviate acid-related symptoms through the inhibition of acid-secreting H+-K+ ATPase. In a number of observational studies, cognitive decline and dementia in the elderly have been linked to the use of PPIs. The precise mechanism underlying this relationship is unknown. These drugs might also alter the pH of brain cells, resulting in the accumulation of amyloid-beta (Aβ) peptides and the development of Alzheimer's disease (AD). Despite the compelling evidence supporting the association of PPIs with dementia, the results of studies remain inconsistent. The absence of a correlation between PPI use and cognitive decline in some studies emphasizes the need for additional research. Chronic PPI use can conceal underlying conditions, including cancer, celiac disease, vitamin B12 deficiency, and renal injury, highlighting dementia risk and the need for further investigations on cognitive health.

Factors associated with diabetes concordant comorbidities among adult diabetic patients in Central Ethiopia: a cross-sectional study

Background

Diabetes comorbidities are a serious public health issue that raises the risk of adverse health effects and complicates diabetes management. It also harms emotional health, medication adherence, self-management, and general quality of life. However, evidence is scarce in Ethiopia, particularly in the study area. Thus, this study aimed to estimate the prevalence of diabetes concordant comorbidities and identify factors associated with the presence of concordant comorbidities among adult diabetic patients in central Ethiopia.

Methods

A health facility-based cross-sectional study was conducted among 398 adult diabetic patients. A computer-generated simple random sampling was used to select study participants. Data were collected using a structured data extraction checklist. The collected data were entered into Epi info version 7.2 and exported to SPSS version 27 for analysis. A binary logistic regression model was used to analyze the association between dependent and independent variables. An adjusted odds ratio with the corresponding 95% confidence interval was used to measure the strength of the association and statistical significance was declared at a p-value < 0.05.

Result

The prevalence of diabetes-concordant comorbidities was 41% (95% CI: 36.2-46.0). The multivariable logistic regression model showed that age 41-60 (AOR = 2.86, 95% CI: 1.60-5.13), place of residence (AOR = 2.22, 95% CI: 1.33-3.70), having type two diabetes (AOR = 3.30, 95% CI: 1.21-8.99), and having positive proteinuria (AOR = 2.64, 95% CI: 1.47-4.76) were significantly associated with diabetes concordant comorbidities.

Conclusion

The prevalence of diabetes-concordant comorbidities was relatively high. Age, place of residence, type of diabetes, and positive proteinuria were factors associated with diabetes-concordant comorbidities. Prevention, early identification, and proper management of diabetes comorbidities are crucial.

The role of the clinical pharmacist in the respiratory or sleep multidisciplinary team

The role of the pharmacist has evolved significantly, not least over the last 20 years. It delivers a skilled profession with a vital role in medicines optimisation and the management of patients with a respiratory or sleep disorder. While pharmacists are capable of acting as independent practitioners delivering direct patient care, this article explores their contribution to multidisciplinary teams within asthma, COPD, cystic fibrosis, tuberculosis, interstitial lung disease and sleep medicine. Having identified patient cohorts needing specialist medicines support, notably those with poor medicines adherence or specific medicines-related needs (for example during adolescence, or women who are pregnant or breastfeeding), these pharmacists work within primary, secondary and specialist tertiary care. The aim of this review is to share and inspire innovative models of working to include more pharmacists in respiratory and sleep medicine.

Hypothyroidism, comorbidity and health-related quality of life: a population-based study

Hypothyroidism is associated with a decreased health-related quality of life (HRQoL). We hypothesized that individuals with hypothyroidism (defined as use of thyroid hormone (TH)) and especially those having an impaired HRQoL are characterized by a high prevalence of comorbid disorders and that the impact of hypothyroidism and comorbidity on HRQoL is synergistic. Presence of comorbidity was based on data obtained using structured questionnaires, physical examination, biochemical measurements and verified medication use. Single morbidities were clustered into 14 different disease domains. HRQoL was measured using the RAND-36. Logistic regression analyses were used to determine the effect of TH use on the odds of having an affected disease domain and a lower score than an age- and sex-specific reference value for HRQoL. TH was used by 4537/14,7201 participants of the population-based Lifelines cohort with a mean (± s.d.) age of 51.0 ± 12.8 years (88% females). Eighty-five percent of the TH users had ≥1 affected disease domain in contrast to 71% of nonusers. TH use was associated with a higher odds of 13 out of 14 affected disease domains independent of age and sex. In a multivariable model, TH use was associated with a decreased HRQoL across six out of eight dimensions. No significant interactions between TH use and affected disease domains were observed. TH users with an impaired HRQoL had significantly more comorbidity than those not having an impaired HRQoL. In this large, population-based study, we demonstrated that TH users had more comorbidity than individuals not using TH. The coexistence of other chronic medical conditions in subjects with TH use led to further lowering of HRQoL in an additive manner.