Lifetime trajectory simulation of chronic disease progression and comorbidity development

Effects of a medication adherence app among medically underserved adults with chronic illness: a randomized controlled trial

For individuals living with a chronic illness who require use of long-term medications, adherence is a vital aspect of successful symptom management and outcomes. This study investigated the effect of a smartphone app on adherence, self-efficacy, knowledge, and medication social support in a medically underserved adult population with various chronic illnesses. Participants were randomized to a group who used the app for one month or a control group provided with a printed medication list. Compared to the control group, participants receiving the intervention had significantly greater medication adherence (Cohen's d = -0.52, p = .014) and medication self-efficacy (Cohen's d = 0.43, p = .035). No significant effects were observed related to knowledge or social support. The findings suggest use of the app could positively impact chronic disease management in a medically underserved population in the United States.

A dynamic probabilistic model of the onset and interaction of cardio-metabolic comorbidities on an ageing adult population

Comorbidity is widespread in the ageing population, implying multiple and complex medical needs for individuals and a public health burden. Determining risk factors and predicting comorbidity development can help identify at-risk subjects and design prevention strategies. Using socio-demographic and clinical data from approximately 11,000 subjects monitored over 11 years in the English Longitudinal Study of Ageing, we develop a dynamic Bayesian network (DBN) to model the onset and interaction of three cardio-metabolic comorbidities, namely type 2 diabetes (T2D), hypertension, and heart problems. The DBN allows us to identify risk factors for developing each morbidity, simulate ageing progression over time, and stratify the population based on the risk of outcome occurrence. By applying hierarchical agglomerative clustering to the simulated, dynamic risk of experiencing morbidities, we identified patients with similar risk patterns and the variables contributing to their discrimination. The network reveals a direct joint effect of biomarkers and lifestyle on outcomes over time, such as the impact of fasting glucose, HbA1c, and BMI on T2D development. Mediated cross-relationships between comorbidities also emerge, showcasing the interconnected nature of these health issues. The model presents good calibration and discrimination ability, particularly in predicting the onset of T2D (iAUC-ROC = 0.828, iAUC-PR = 0.294) and survival (iAUC-ROC = 0.827, iAUC-PR = 0.311). Stratification analysis unveils two distinct clusters for all comorbidities, effectively discriminated by variables like HbA1c for T2D and age at baseline for heart problems. The developed DBN constitutes an effective, highly-explainable predictive risk tool for simulating and stratifying the dynamic risk of developing cardio-metabolic comorbidities. Its use could help identify the effects of risk factors and develop health policies that prevent the occurrence of comorbidities.

Effect of a Medication Adherence Mobile Phone App on Medically Underserved Patients with Chronic Illness: Preliminary Efficacy Study

BACKGROUND

Medication adherence is vital in the treatment of patients with chronic illness who require long-term medication therapies to maintain optimal health. Medication adherence, a complex and widespread problem, has been difficult to solve. Additionally, lower-income, medically underserved communities have been found to have higher rates of inadequate adherence to oral medications. Even so, this population has been underrepresented in studies using mobile medication adherence app interventions. Federally qualified health centers provide care for medically underserved populations, defined as communities and populations where there is a demonstrable unmet need for health services. These centers have been reporting an increase in a more complex chronic disease population. Including medically underserved individuals in mobile health studies provides opportunities to support this disproportionately affected group, work toward reducing health disparities in access to health care, and understand barriers to mobile health uptake.

OBJECTIVE

The aim of this preliminary efficacy study was to evaluate the effects and feasibility of a commercially available medication adherence app, Medisafe, in a medically underserved adult population with various chronic illnesses seeking care in a federally qualified health center.

METHODS

Participants in this single-arm pre-post intervention preliminary efficacy study (N=10) completed a baseline survey, used the app for 2 weeks, and completed an end-of-study survey. The primary outcome measures were medication adherence and medication self-efficacy. Feedback on the use of the app was also gathered.

RESULTS

A statistically significant median increase of 8 points on the self-efficacy for adherence to medications scale was observed (P=.03, Cohen d=0.69). Though not significant, the adherence to refills and medications scale demonstrated a median change of 2.5 points in the direction of increased medication adherence (P=.21, Cohen d=0.41). Feedback about the app was positive.

CONCLUSIONS

Use of the Medisafe app is a viable option to improve medication self-efficacy and medication adherence in medically underserved patients in an outpatient setting with a variety of chronic illnesses.

Understanding human aging and the fundamental cell signaling link in age-related diseases: the middle-aging hypovascularity hypoxia hypothesis

Aging-related hypoxia, oxidative stress, and inflammation pathophysiology are closely associated with human age-related carcinogenesis and chronic diseases. However, the connection between hypoxia and hormonal cell signaling pathways is unclear, but such human age-related comorbid diseases do coincide with the middle-aging period of declining sex hormonal signaling. This scoping review evaluates the relevant interdisciplinary evidence to assess the systems biology of function, regulation, and homeostasis in order to discern and decipher the etiology of the connection between hypoxia and hormonal signaling in human age-related comorbid diseases. The hypothesis charts the accumulating evidence to support the development of a hypoxic milieu and oxidative stress-inflammation pathophysiology in middle-aged individuals, as well as the induction of amyloidosis, autophagy, and epithelial-to-mesenchymal transition in aging-related degeneration. Taken together, this new approach and strategy can provide the clarity of concepts and patterns to determine the causes of declining vascularity hemodynamics (blood flow) and physiological oxygenation perfusion (oxygen bioavailability) in relation to oxygen homeostasis and vascularity that cause hypoxia (hypovascularity hypoxia). The middle-aging hypovascularity hypoxia hypothesis could provide the mechanistic interface connecting the endocrine, nitric oxide, and oxygen homeostasis signaling that is closely linked to the progressive conditions of degenerative hypertrophy, atrophy, fibrosis, and neoplasm. An in-depth understanding of these intrinsic biological processes of the developing middle-aged hypoxia could provide potential new strategies for time-dependent therapies in maintaining healthspan for healthy lifestyle aging, medical cost savings, and health system sustainability.

The Italian Framework of Bipolar Disorders in the Elderly: Old and Current Issues and New Suggestions for the Geriatric Psycho-Oncology Research

BACKGROUND

Older adults with mood disorders constitute a heterogeneous group in a complex spectrum interlinked with physical comorbidities. Worldwide, Bipolar disorders in older people (OABD) remain underestimated and underdiagnosed. OABD is challenging in the clinical setting and is associated with adverse outcomes (increased risk of anti-social behaviour triggered by inappropriate drugs and increased incidence of health deficits, including cancer). This article aims to describe the state of the art of OABD in the Italian framework and provide a new field of research.

METHODS

We performed an overview of the literature, selecting our target population (over 65 years) and synthesising the main challenging issues. By exploiting the Italian database from the Minister of Health in 2021, we analysed epidemiological data in the age range 65-74 years and 75-84 years old.

RESULTS

Females showed the highest prevalence and incidence in both groups, with a regional difference across the country but more evident in the Autonomous Provinces of Bolzano and Trento for the 65-74 years range. Several projects recently focused on this topic, and the urgency to define better the epidemiological framework is mandatory.

CONCLUSIONS

This study represented the first attempt to report the comprehensive Italian framework on OABD aimed at fostering research activities and knowledge.

Factors impacting medication adherence in a birth cohort at higher risk for Hepatitis C infection

Due to the high prevalence of Hepatitis C virus (HCV) infection among individuals born between 1945 and 1965, in 2012 the Centers for Disease Control and Prevention began recommending HCV screening for this birth cohort. As adherence to HCV treatment is essential for sustained virologic response, identifying factors influencing medication adherence is important. The validated Adherence to Refills and Medications Scale (ARMS) is used to study recent medication adherence in those with chronic disease. This cross-sectional pilot study assesses factors associated with reduced adherence, indicated by higher ARMS scores, among individuals in this birth cohort. To elucidate factors associated with medication adherence, measured by the ARMS score, among a birth cohort at higher risk for HCV to guide future treatment and improve adherence. Patients born between 1945 and 1965, accessing care at an academic family medicine clinic, were recruited between April and June 2019. Demographics, prior HCV diagnosis, HCV risk factors (prior imprisonment, tattoos, and intravenous drug use), depression assessment (Patient Health Questionnaire-9), adverse childhood experiences (ACEs), and ARMS scores were collected. Mean ARMS scores were compared using t tests and analysis of variance (α = 0.05), while multiple variable models were performed using linear regression. Women comprised 58% of participants (n = 76), 52% reported depression and 37% 4 or more ACEs. The mean ARMS score was 16.3 (SD = 3.43) and 10% reported prior diagnosis of HCV. In the final multiple variable model, ARMS scores were 2.3 points higher in those with mild depression (95% CI: 0.63, 4.04), 2.0 in those with at least 4 ACEs (95% CI: 0.55, 3.49), and 1.8 in those with tattoos (95% CI: 0.30, 3.28). ACEs and food insecurity were identified as confounding variables in those with moderate to severe depression. This study found medication adherence was related to depression, ACEs, tattoos, and food insecurity among patients in this birth cohort at higher risk for HCV.

The 10-year multimorbidity trajectory and mortality risk in older people with long-term care needs

BACKGROUND

This study aimed to identify trajectories of multimorbidity in older adults prior to receiving long-term care benefits and to demonstrate their value in predicting mortality.

METHODS

This study included 1,004,924 Korean beneficiaries who completed the National Long-Term Care Insurance (NLTCI) eligibility assessment between 2010 and 2016. Multimorbidity was defined as the coexistence of 2 or more out of 23 chronic diseases related to disability in the 10 years before transitioning to long-term care. Mortality was defined as all-cause deaths after the date of the NLTCI needs assessment. Latent class growth modeling was performed to identify groups that exhibited similar trajectory patterns over time. Sex, age, and long-term care grade were used as covariates. Cox proportional hazards models were used to analyze the mortality rates by trajectories.

RESULTS

Three patterns emerged in the multimorbidity trajectory in the 10 years prior to entering the long-term care system: consistently low morbidity ("consistently low"), an abrupt increase in morbidity in less than one year ("catastrophic"), and an increment in morbidity over a longer period ("progressive"). In multiple Cox regression adjusting for covariates, the hazard ratios (95% confidence interval) of 1-year mortality for the catastrophic and progressive groups were 1.38 (1.36-1.39) and 1.43 (1.41-1.45), respectively, compared to the consistently low group.

CONCLUSIONS

This study identified distinct trajectories of multimorbidity in older people accessing the long-term care system and demonstrated their prognostic value for the survival of those with long-term care needs. Treatment and management strategies targeting individuals with a high-risk trajectory are warranted.

Patterns of cardiometabolic multimorbidity and the risk of depressive symptoms in a longitudinal cohort of middle-aged and older Chinese

BACKGROUND

Cardiometabolic diseases (CMDs) are associated with depression. However, it is unclear whether coexisting CMDs may increase the risk of depression. We examined associations between cardiometabolic multimorbidity and depressive symptoms among middle-aged and older Chinese.

METHODS

Participants aged ≥45 years were enrolled from the China Health and Retirement Longitudinal Study 2011-2018 (N = 18,002). Cardiometabolic multimorbidity was defined as the coexistence of ≥2 CMDs, including stroke, heart disease, diabetes, hypertension, and dyslipidemia. Depressive symptoms were assessed by the Center for Epidemiological Studies Depression Scale. We used generalized estimating equation models to examine associations between cardiometabolic multimorbidity and depressive symptoms, including the dose effect of disease count and prevalent disease combinations, as well as individual and additive effects of specific CMDs.

RESULTS

The prevalence of cardiometabolic multimorbidity was 24.5%. A higher number of CMDs had an additive dose effect on depressive symptoms that persisted consistently in specific CMDs. Stroke only, heart disease only, and diabetes only were each associated with a higher risk of depressive symptoms compared with no CMDs. CMD combinations involving stroke, heart disease, or diabetes were each associated with an increased risk of depressive symptoms compared with the absence of stroke, heart disease, or diabetes.

LIMITATION

Self-reported chronic conditions.

CONCLUSION

Stroke, heart disease, and diabetes showed individual and additive effects on CMD combinations, whereas hypertension and dyslipidemia only showed associations with depressive symptoms in combinations with other CMDs. These results suggest person-centered healthcare of mental health prevention and treatment for middle-aged and older adults with individual or multiple CMDs.

Clinical Variation in the Treatment Practices for Medication Nonadherence, Drug-Drug Interactions, and Recognition of Disease Progression in Patients with Chronic Cardiometabolic Diseases: A Cross-Sectional Patient Simulation Study among Primary Care Physicians

BACKGROUND

Medication nonadherence in patients with chronic diseases is common, costly, and often underdiagnosed. In the United States, approximately 40-50% of patients with cardiometabolic conditions are not adherent to long-term medications. Drug-drug interactions (DDI) are also underrecognized and may lead to medication nonadherence in this patient population. Treatment complexity associated with cardiometabolic conditions contributes to increased risk for adverse drug events and DDIs.

METHODS

We recruited a nationally representative sample of 246 board-certified family and internal medicine physicians to evaluate how they assessed, identified, and treated medication nonadherence, DDIs, and worsening disease. Participating physicians were asked to care for three online simulated patients, each with at least one chronic cardiometabolic disease, including atrial fibrillation, heart failure, diabetes mellitus, or hypertension, and who were taking prescription medications for their disease. Physicians' scores were based on evidence-based care recommendation criteria, including overall care quality and treatment for medication nonadherence and DDIs.

RESULTS

Overall, quality-of-care scores across all cases ranged from 13% to 87% with an average of 50.8% ± 12.1%. The average overall diagnostic plus treatment score was 21.9% ± 13.6%. Participants identified nonadherence in just 3.6% of cases, DDIs in 8.9% of cases, and disease progression in 30.3% of cases.

CONCLUSIONS

Based on these study results, primary care physicians were unable to adequately diagnose and treat patients with chronic cardiometabolic diseases who either suffered from medication nonadherence, DDIs, or progression of the disease. Improved standardization and technique in identifying these diagnoses is needed in primary care. Trial Registration. This trial is registered with clinicaltrials.gov, NCT05192590.

Inequalities in developing multimorbidity over time: A population-based cohort study from an urban, multi-ethnic borough in the United Kingdom

Background

Social and material deprivation accelerate the development of multimorbidity, yet the mechanisms which drive multimorbidity pathways and trajectories remain unclear. We aimed to examine the association between health inequality, risk factors and accumulation or resolution of LTCs, taking disease sequences into consideration.

Methods

We conducted a retrospective cohort of adults aged 18 years and over, registered between April 2005 and May 2020 in general practices in one inner London borough (n = 826,936). Thirty-two long term conditions (LTCs) were selected using a consensus process, based on a definition adapted to the demographic characteristics of the local population. sThe development and resolution of these LTCs were examined according to sociodemographic and clinical risk factors (hypertension; moderate obesity (BMI 30·0-39·9 kg/m2), high cholesterol (total cholesterol > 5 mmol/L), smoking, high alcohol consumption (>14 units per week), and psychoactive substance use), through the application of multistate Markov chain models.

Findings

Participants were followed up for a median of 4.2 years (IQR = 1·8 - 8·4); 631,760 (76%) entered the study with no LTCs, 121,424 (15%) with 1 LTC, 41,720 (5%) with 2 LTCs, and 31,966 (4%) with three or more LTCs. At the end of follow-up, 194,777 (24%) gained one or more LTCs, while 45,017 (5%) had resolved LTCs and 27,021 (3%) died. In multistate models, deprivation (hazard ratio [HR] between 1·30 to 1·64), female sex (HR 1·13 to 1·20), and Black ethnicity (HR 1·20 to 1·30; vs White) were independently associated with increased risk of transition from one to two LTCs, and shorter time spent in a healthy state. Substance use was the strongest risk factor for multimorbidity with an 85% probability of gaining LTCs over the next year. First order Markov chains identified consistent disease sequences including: chronic pain or osteoarthritis followed by anxiety and depression; alcohol and substance dependency followed by HIV, viral hepatitis, and liver disease; and morbid obesity followed by diabetes, hypertension, and chronic pain.

Interpretation

We examined the relations among 32 LTCs, taking the order of disease occurrence into consideration. Distinctive patterns for the development and accumulation of multimorbidity have emerged, with increased risk of transitioning from no conditions to multimorbidity and mortality related to ethnicity, deprivation and gender. Musculoskeletal disorders, morbid obesity and substance abuse represent common entry points to multimorbidity trajectories.

Studying trajectories of multimorbidity: a systematic scoping review of longitudinal approaches and evidence

OBJECTIVES

Multimorbidity-the co-occurrence of at least two chronic diseases in an individual-is an important public health challenge in ageing societies. The vast majority of multimorbidity research takes a cross-sectional approach, but longitudinal approaches to understanding multimorbidity are an emerging research area, being encouraged by multiple funders. To support development in this research area, the aim of this study is to scope the methodological approaches and substantive findings of studies that have investigated longitudinal multimorbidity trajectories.

DESIGN

We conducted a systematic search for relevant studies in four online databases (Medline, Scopus, Web of Science and Embase) in May 2020 using predefined search terms and inclusion and exclusion criteria. The search was complemented by searching reference lists of relevant papers. From the selected studies, we systematically extracted data on study methodology and findings and summarised them in a narrative synthesis.

RESULTS

We identified 35 studies investigating multimorbidity longitudinally, all published in the last decade, and predominantly in high-income countries from the Global North. Longitudinal approaches employed included constructing change variables, multilevel regression analysis (eg, growth curve modelling), longitudinal group-based methodologies (eg, latent class modelling), analysing disease transitions and visualisation techniques. Commonly identified risk factors for multimorbidity onset and progression were older age, higher socioeconomic and area-level deprivation, overweight and poorer health behaviours.

CONCLUSION

The nascent research area employs a diverse range of longitudinal approaches that characterise accumulation and disease combinations and to a lesser extent disease sequencing and progression. Gaps include understanding the long-term, life course determinants of different multimorbidity trajectories, and doing so across diverse populations, including those from low-income and middle-income countries. This can provide a detailed picture of morbidity development, with important implications from a clinical and intervention perspective.