Identifying multimorbidity clusters with the highest primary care use: 15 years of evidence from a multi-ethnic metropolitan population

Chronic Disease Patterns and Their Relationship With Health-Related Quality of Life in South Korean Older Adults With the 2021 Korean National Health and Nutrition Examination Survey: Latent Class Analysis

BACKGROUND

Improved life expectancy has increased the prevalence of older adults living with multimorbidities, which likely deteriorates their health-related quality of life (HRQoL). Understanding which chronic conditions frequently co-occur can facilitate person-centered care tailored to the needs of individuals with specific multimorbidity profiles.

OBJECTIVE

The study objectives were to (1) examine the prevalence of multimorbidity among Korean older adults (ie, those aged 65 years and older), (2) investigate chronic disease patterns using latent class analysis, and (3) assess which chronic disease patterns are more strongly associated with HRQoL.

METHODS

A sample of 1806 individuals aged 65 years and older from the 2021 Korean National Health and Nutrition Examination Survey was analyzed. Latent class analysis was conducted to identify the clustering pattern of chronic diseases. HRQoL was assessed by an 8-item health-related quality of life scale (HINT-8). Multiple linear regression was used to analyze the association with the total score of the HINT-8. Logistic regression analysis was performed to evaluate the odds ratio of having problems according to the HINT-8 items.

RESULTS

The prevalence of multimorbidity in the sample was 54.8%. Three chronic disease patterns were identified: relatively healthy, cardiometabolic condition, arthritis, allergy, or asthma. The total scores of the HINT-8 were the highest in participants characterized as arthritis, allergy, or asthma group, indicating the lowest quality of life.

CONCLUSIONS

Current health care models are disease-oriented, meaning that the management of chronic conditions applies to a single condition and may not be relevant to those with multimorbidities. Identifying chronic disease patterns and their impact on overall health and well-being is critical for guiding integrated care.

Multimorbidity and use of health services in a population diagnosed with COVID-19 in a municipality in the Southern Region of Brazil, 2020-2021: a cross-sectional study

OBJECTIVE

To assess association between multimorbidity and use of health services in a population diagnosed with COVID-19, in southern Brazil.

METHODS

This was a cross-sectional study with data from a longitudinal study carried out in the city of Rio Grande, Rio Grande do Sul, Brazil, in 2021 with all adult individuals diagnosed with COVID-19; descriptive analyses were performed and presented as proportions with 95% confidence intervals (95%CI); Poisson regression was performed and reported as prevalence ratios (PR) in order to assess association between multimorbidity (3 or more diseases) and healthcare service use.

RESULTS

In total, 2,919 participants were included, of which 40.4% had multimorbidity (≥ 2 diseases); the adjusted results showed that individuals with multimorbidity were more likely to use most of the services assessed, PR = 3.21 (95%CI 1.40;7.37), for Emergency Rooms.

CONCLUSION

Multimorbidity was associated with using different types of health services.

Disease patterns in high-cost individuals with multimorbidity: a retrospective cross-sectional study in primary care

BACKGROUND

'High-cost' individuals with multimorbidity account for a disproportionately large share of healthcare costs and are at most risk of poor quality of care and health outcomes.

AIM

To compare high-cost with lower-cost individuals with multimorbidity and assess whether these populations can be clustered based on similar disease patterns.

DESIGN AND SETTING

A cross-sectional study based on 2019/2020 electronic medical records from adults registered to primary care practices (n = 41) in a London borough.

METHOD

Multimorbidity is defined as having ≥2 long-term conditions (LTCs). Primary care costs reflected consultations, which were costed based on provider and consultation types. High cost was defined as the top 20% of individuals in the cost distribution. Descriptive analyses identified combinations of 32 LTCs and their contribution to costs. Latent class analysis explored clustering patterns.

RESULTS

Of 386 238 individuals, 101 498 (26%) had multimorbidity. The high-cost group (n = 20 304) incurred 53% of total costs and had 6833 unique disease combinations, about three times the diversity of the lower-cost group (n = 81 194). The trio of anxiety, chronic pain, and depression represented the highest share of costs (5%). High-cost individuals were best grouped into five clusters, but no cluster was dominated by a single LTC combination. In three of five clusters, mental health conditions were the most prevalent.

CONCLUSION

High-cost individuals with multimorbidity have extensive heterogeneity in LTCs, with no single LTC combination dominating their primary care costs. The frequent presence of mental health conditions in this population supports the need to enhance coordination of mental and physical health care to improve outcomes and reduce costs.

Utilization of Primary Healthcare Services in Patients with Multimorbidity According to Their Risk Level by Adjusted Morbidity Groups: A Cross-Sectional Study in Chamartín District (Madrid)

Patients with multimorbidity have increased and more complex healthcare needs, posing their management a challenge for healthcare systems. This study aimed to describe their primary healthcare utilization and associated factors. A population-based cross-sectional study was conducted in a Spanish basic healthcare area including all patients with chronic conditions, differentiating between having multimorbidity or not. Sociodemographic, functional, clinical and service utilization variables were analyzed, stratifying the multimorbid population by the Adjusted Morbidity Groups (AMG) risk level, sex and age. A total of 6036 patients had multimorbidity, 64.2% being low risk, 28.5% medium risk and 7.3% high risk. Their mean age was 64.1 years and 63.5% were women, having on average 3.5 chronic diseases, and 25.3% were polymedicated. Their mean primary care contacts/year was 14.9 (7.8 with family doctors and 4.4 with nurses). Factors associated with primary care utilization were age (B-coefficient [BC] = 1.15;95% Confidence Interval [CI] = 0.30-2.01), female sex (BC = 1.04; CI = 0.30-1.78), having a caregiver (BC = 8.70; CI = 6.72-10.69), complexity (B-coefficient = 0.46; CI = 0.38-0.55), high-risk (B-coefficient = 2.29; CI = 1.26-3.32), numerous chronic diseases (B-coefficient = 1.20; CI = 0.37-2.04) and polypharmacy (B-coefficient = 5.05; CI = 4.00-6.10). This study provides valuable data on the application of AMG in multimorbid patients, revealing their healthcare utilization and the need for a patient-centered approach by primary care professionals. These results could guide in improving coordination among professionals, optimizing multimorbidity management and reducing costs derived from their extensive healthcare utilization.

Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland

There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.

Primary care physicians' work conditions and their confidence in managing multimorbidity: a quantitative analysis using Job Demands-Resources Model

BACKGROUND

Multimorbidity is a global issue that presents complex challenges for physicians, patients, and health systems. However, there is a lack of research on the factors that influence physicians' confidence in managing multimorbidity within primary care settings, particularly regarding physicians' work conditions.

OBJECTIVES

Drawing on the Job Demands-Resources Model, this study aims to investigate the level of confidence among Chinese primary care physicians in managing multimorbidity and examine the predictors related to their confidence.

METHODS

Data were collected from 224 physicians working in 38 Community Healthcare Centres (CHCs) in Shanghai, Shenzhen, Tianjin, and Jinan, China. Work-family conflict (WFC) perceived organizational support (POS), self-directed learning (SDL), and burnout were measured. Physicians' confidence was assessed using a single item. Mediation effect analysis was conducted using the Baron and Kenny method.

RESULTS

The results showed that the mean confidence score for physicians managing multimorbidity was 3.63 out of 5, only 20.10% rating their confidence level as 5. WFC negatively related physicians' confidence and POS positively related physicians' confidence in multimorbid diagnosis and treatment. Burnout fully mediated the relationship between WFC and physicians' confidence, and SDL partially mediated the relationship between POS and physicians' confidence.

CONCLUSIONS

The confidence level of Chinese primary care physicians in managing multimorbidity needs improvement. To enhance physicians' confidence in managing multimorbid patients, CHCs in China should address WFC and burnout and promote POS and SDL.

Identifying populations with chronic pain in primary care: developing an algorithm and logic rules applied to coded primary care diagnostic and medication data

BACKGROUND

Estimates of chronic pain prevalence using coded primary care data are likely to be substantially lower than estimates derived from community surveys. Most primary care studies have estimated chronic pain prevalence using data searches confined to analgesic medication prescriptions. Increasingly, following recent NICE guideline recommendations, patients and doctors opt for non-drug treatment of chronic pain thus excluding these patients from prevalence estimates based on medication codes. We aimed to develop and test an algorithm combining medication codes with selected diagnostic codes to estimate chronic pain prevalence using coded primary care data.

METHODS

Following a scoping review 4 criteria were developed to identify cohorts of people with chronic pain. These were (1) people with one of 12 ('tier 1') conditions that almost always results in the individual having chronic pain (2) people with one of 20 ('tier 2') conditions included when there are also 3 or more prescription-only analgesics issued in the last 12 months (3) chronic neuropathic pain, or (4) 4 or more prescription-only analgesics issued in the last 12 months. These were translated into 8 logic rules which included 1,932 SNOMED CT codes.

RESULTS

The algorithm was run on primary care data from 41 GP Practices in Lambeth. The total population consisted of 386,238 GP registered adults ≥ 18 years as of the 31st March 2021. 64,135 (16.6%) were identified as people with chronic pain. This definition demonstrated notably high rates in Black ethnicity females, and higher rates in the most deprived, and older population.

CONCLUSIONS

Estimates of chronic pain prevalence using structured healthcare data have previously shown lower prevalence estimates for chronic pain than reported in community surveys. This has limited the ability of researchers and clinicians to fully understand and address the complex multifactorial nature of chronic pain. Our study demonstrates that it may be possible to establish more representative prevalence estimates using structured data than previously possible. Use of logic rules offers the potential to move systematic identification and population-based management of chronic pain into mainstream clinical practice at scale and support improved management of symptom burden for people experiencing chronic pain.

Cohort profile: The South London Stroke Register - a population-based register measuring the incidence and outcomes of stroke

PURPOSE

The South London Stroke Register (SLSR) is a population-based cohort study, which was established in 1995 to study the causes, incidence, and outcomes of stroke. The SLSR aims to estimate incidence, and acute and long term needs in a multi-ethnic inner-city population, with follow-up durations for some participants exceeding 20 years.

PARTICIPANTS

The SLSR aims to recruit residents of a defined area within Lambeth and Southwark who experience a first stroke. More than 7700 people have been registered since inception, and >2750 people continue to be followed up. At the 2011 census, the source population was 357,308.

FINDINGS TO DATE

The SLSR was instrumental in highlighting the inequalities in risk and outcomes in the UK, and demonstrating the dramatic improvements in care quality and outcomes in recent decades. Data from the SLSR informed the UK National Audit Office in its 2005 report criticising the poor state of stroke care in England. For people living in the SLSR area the likelihood of being treated in a stroke unit increased from 19% in 1995-7 to 75% in 2007-9. The SLSR has investigated health inequalities in stroke incidence and outcome. SLSR analyses have demonstrated that lower socioeconomic status was associated with poorer outcome, and that Black people and younger people have not experienced the same improvements in stroke incidence as other groups.

FUTURE PLANS

As part of an NIHR Programme Grant for Applied Research, from April 2022 the SLSR has expanded to recruit ICD-11 defined stroke (including those with <24 h symptoms where there are neuroimaging findings), and have expanded the follow up interviews to collect more detailed information on quality of life, cognition, and care needs. Additional data items will be added over the Programme based on feedback from patients and other stakeholders.

Multimorbidity Patterns and Associations with Gait, Balance and Lower Extremity Muscle Function in the Elderly: A Cross-Sectional Study in Northwest China

Purpose

Fall is a common geriatric syndrome leading to various adverse outcomes in the elderly. Gait and balance disorders and decreased lower extremity muscle function are the major intrinsic risk factors of falls, and studies suggested that they were closely related to the underlying chronic conditions. This study aimed to explore the patterns of multimorbidity and determine the associations of these multimorbidity patterns with gait, balance and lower extremity muscle function.

Patients and Methods

A cross-sectional survey of 4803 participants aged ≥60 years in Shaanxi Province, China was conducted and the self-reported chronic conditions were investigated. The 6-m walk test, timed-up-and-go test (TUG) and 5-sit-to-stand test (5-STS) were conducted to evaluate gait, balance, and lower extremity muscle function respectively. Latent class analysis was used to explore patterns of multimorbidity, and multivariate regression analysis was used to determine the associations of multimorbidity patterns with gait, balance, and lower extremity muscle function.

Results

Five multimorbidity patterns were identified: Degenerative Disease Class, Cardio-metabolic Class, Stroke-Respiratory-Depression Class, Gastrointestinal Class, and Very sick Class, and they were differently associated with gait and balance disorders and decreased lower extremity muscle function. In particular, the multimorbidity patterns of Degenerative Disease Class and Stroke-Respiratory-Depression Class were closely associated with all the three risk factors of falls.

Conclusion

There are significant differences in the impact of different multimorbidity patterns on the major intrinsic risk factors of falls in the elderly population, and appropriate multimorbidity patterns are closely related to the prediction of falls and can help to develop fall prevention strategies in the elderly.

Prevalence and global trends of polypharmacy in patients with chronic liver disease: A systematic review and meta-analysis

BACKGROUND

Despite its central role in drug metabolism, the exact prevalence estimates and factors affecting global trends of polypharmacy in patients with chronic liver disease (CLD) have remained unexamined. The aim of this systematic review and meta-analysis is to estimate the prevalence of polypharmacy in patients with CLD and to comprehensively synthesize the socio-demographic factors that drive this.

METHODS

We conducted a comprehensive search of relevant databases (PubMed, EMBASE, Science citation index, Cochrane Database of Systematic Reviews, and database of abstracts of reviews of effectiveness) for studies published from inception to May 30, 2022 that reported on prevalence estimates of polypharmacy in patients with CLD. The risk of bias was conducted utilizing Loney criteria. The primary outcome was the pooled prevalence of polypharmacy in patients with CLD. We subsequently performed a systematic review and weighted meta-analysis to ascertain the exact pooled prevalence of polypharmacy among patients with CLD.

RESULTS

We identified approximately 50 studies from the initial literature search, of which 7 (enrolling N = 521,435 patients) with CLD met the inclusion criteria; of these, 58.7% were male, with a mean age of 53.9 (SD ± 12.2) years. The overall pooled prevalence of polypharmacy among patients with CLD was 31% (95% confidence interval [CI]: 4%-66%, I2 = 100%, τ2 ≤ 0.001, P ≤ .0001). We found higher pooled prevalence estimates among patients aged 50 years and older compared to their younger cohorts (42%, [CI 10-77]; I2 = 100%, P = <.001 vs 21%, [CI 0-70]; I2 = 100%, P = <.001).

CONCLUSION

In an examination of multiple community- and hospital-based databases of patients with CLD, we found a pooled prevalence estimate of polypharmacy of approximately 31%. This represents a case burden within the range reported in the general population and will likely respond to mitigation strategies employed thus far for patients in that population.

Multimorbidity patterns and mortality in older adults: Results from the KORA-Age study

The coexistence of several chronic diseases is very common in older adults, making it crucial to understand multimorbidity (MM) patterns and associated mortality. We aimed to determine the prevalence of MM and common chronic disease combinations, as well as their impact on mortality in men and women aged 65 years and older using the population-based KORA-Age study, based in South of Germany. The chronic disease status of the participants was determined in 2008/9, and mortality status was followed up until 2016. MM was defined as having at least two chronic diseases. We used Cox proportional hazard models to calculate the hazard ratios (HRs) and the 95% confidence intervals (CIs) for associations between MM and all-cause mortality. During the study period 495 men (24.6%) and 368 women (17.4%) died. Although the MM prevalence was almost the same in men (57.7%) and women (60.0%), the overall effect of MM on mortality was higher in men (HR: 1.81, 95% CI: 1.47–2.24) than in women (HR: 1.28, 95% CI: 1.01–1.64; p-value for interaction &lt;0.001). The type of disease included in the MM patterns had a significant impact on mortality risk. For example, when both heart disease and diabetes were included in the combinations of two and three diseases, the mortality risk was highest. The risk of premature death does not only depend on the number of diseases but also on the specific disease combinations. In this study, life expectancy depended strongly on a few diseases, such as diabetes, hypertension, and heart disease.

Multimorbidity and comorbidity patterns in the English National Health Service

In an observational population-based study including nearly four million participants, Kuan et al. examined frequencies of common combinations of diseases and identified non-random disease associations in people of all ages and multiple ethnicities.

Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study

Background

Multimorbidity represents a global health challenge, which requires a more global understanding of multimorbidity patterns and trends. However, the majority of studies completed to date have often relied on self-reported conditions, and a simultaneous assessment of the entire spectrum of chronic disease co-occurrence, especially in developing regions, has not yet been performed.

Objective

We attempted to provide a multidimensional approach to understand the full spectrum of chronic disease co-occurrence among general inpatients in southwest China, in order to investigate multimorbidity patterns and temporal trends, and assess their age and sex differences.

Methods

We conducted a retrospective cohort analysis based on 8.8 million hospital discharge records of about 5.0 million individuals of all ages from 2015 to 2019 in a megacity in southwest China. We examined all chronic diagnoses using the ICD-10 (International Classification of Diseases, 10th revision) codes at 3 digits and focused on chronic diseases with ≥1% prevalence for each of the age and sex strata, which resulted in a total of 149 and 145 chronic diseases in males and females, respectively. We constructed multimorbidity networks in the general population based on sex and age, and used the cosine index to measure the co-occurrence of chronic diseases. Then, we divided the networks into communities and assessed their temporal trends.

Results

The results showed complex interactions among chronic diseases, with more intensive connections among males and inpatients ≥40 years old. A total of 9 chronic diseases were simultaneously classified as central diseases, hubs, and bursts in the multimorbidity networks. Among them, 5 diseases were common to both males and females, including hypertension, chronic ischemic heart disease, cerebral infarction, other cerebrovascular diseases, and atherosclerosis. The earliest leaps (degree leaps ≥6) appeared at a disorder of glycoprotein metabolism that happened at 25-29 years in males, about 15 years earlier than in females. The number of chronic diseases in the community increased over time, but the new entrants did not replace the root of the community.

Conclusions

Our multimorbidity network analysis identified specific differences in the co-occurrence of chronic diagnoses by sex and age, which could help in the design of clinical interventions for inpatient multimorbidity.

Multimorbidity and mortality: A data science perspective

Background

With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide.

Methods

Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005–2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs based on single snapshots of closed cohorts, we adopt a longitudinal approach to examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality.

Results

The proportion of the population with multimorbidity has increased by approximately 2.5 percentage points over the last decade, with more than 17% having at least two chronic morbidities. We find that the prevalence and the severity of multimorbidity, as quantified by the number of co-occurring chronic conditions, increase progressively with age. Stratifying by socioeconomic status, we find that people living in more deprived areas are more likely to be multimorbid compared to those living in more affluent areas at all ages. The same trend holds consistently for all years in our data. In general, hypertension, diabetes, and respiratory-related diseases demonstrate high in-degree centrality and eigencentrality, while cardiac disorders show high out-degree centrality.

Conclusions

We use data-driven methods to characterize multimorbidity patterns in different demographic groups and their evolution over the past decade. In addition to a number of strongly associated comorbid pairs (e.g., cardiac-vascular and cardiac-metabolic disorders), we identify three principal clusters: a respiratory cluster, a cardiovascular cluster, and a mixed cardiovascular-renal-metabolic cluster. These are supported by established pathophysiological mechanisms and shared risk factors, and largely confirm and expand on the results of existing studies in the medical literature. Our findings contribute to a more quantitative understanding of the epidemiology of multimorbidity, an important pre-requisite for developing more effective medical care and policy for multimorbid patients.