Performance of quantitative measures of multimorbidity: a population-based retrospective analysis

Comorbidities and use of health services in people with diabetes mellitus according to risk levels by adjusted morbidity groups

BACKGROUND

People with diabetes mellitus frequently have other comorbidities and involve greater use of primary and hospital care services. The aim of this study was to describe the comorbidities and use of primary and hospital care services of people with diabetes according to their risk level by adjusted morbidity groups (AMG) and to analyse the factors associated with the utilisation of these services.

METHODS

Cross-sectional study. People with diabetes were identified within the population of patients with chronic conditions of an urban health care centre by the AMG stratification tool integrated into the primary health care electronic clinical record of the Community of Madrid. Sociodemographic, functional, clinical characteristics and annual health care services utilisation variables were collected. Univariate, bivariate and Poisson regression analyses were performed.

RESULTS

A total of 1,063 people with diabetes were identified, representing 10.8% of patients with chronic conditions within the health centre. A total of 51.4% were female, the mean age was 70 years, 94.4% had multimorbidity. According to their risk level, 17.8% were high-risk, 40.6% were medium-risk and 41.6% were low-risk. The most prevalent comorbidities were hypertension (70%), dyslipidaemia (67%) and obesity (32.4%). Almost 50% were polymedicated. Regarding health services utilisation, 94% were users of primary care, and 59.3% were users of hospital care. Among the main factors associated with the utilisation of both primary and hospital care services were AMG risk level and complexity index. In primary care, utilisation was also associated with the need for primary caregivers, palliative care and comorbidities such as chronic heart failure and polymedication, while in hospital care, utilisation was also associated with comorbidities such as cancer, chronic obstructive pulmonary disease or depression.

CONCLUSIONS

People with diabetes were older, with important needs for care, many associated comorbidities and polypharmacy that increased in parallel with the patient's risk level and complexity. The utilisation of primary and hospital care services was very high, being more frequent in primary care. Health services utilization were principally associated with functional factors related to the need of care and with clinical factors such as AMG medium and high-risk level, more complexity index, some serious comorbidities and polymedication.

Predicting healthcare expenditure based on Adjusted Morbidity Groups to implement a needs-based capitation financing system

BACKGROUND

Due to population aging, healthcare expenditure is projected to increase substantially in developed countries like Spain. However, prior research indicates that health status, not merely age, is a key driver of healthcare costs. This study analyzed data from over 1.25 million residents of Spain's Murcia region to develop a capitation-based healthcare financing model incorporating health status via Adjusted Morbidity Groups (AMGs). The goal was to simulate an equitable area-based healthcare budget allocation reflecting population needs.

METHODS

Using 2017 data on residents' age, sex, AMG designation, and individual healthcare costs, generalized linear models were built to predict healthcare expenditure based on health status indicators. Multiple link functions and distribution families were tested, with model selection guided by information criteria, residual analysis, and goodness-of-fit statistics. The selected model was used to estimate adjusted populations and simulate capitated budgets for the 9 healthcare districts in Murcia.

RESULTS

The gamma distribution with logarithmic link function provided the best model fit. Comparisons of predicted and actual average costs revealed underfunded and overfunded areas within Murcia. If implemented, the capitation model would decrease funding for most districts (up to 15.5%) while increasing it for two high-need areas, emphasizing allocation based on health status and standardized utilization rather than historical spending alone.

CONCLUSIONS

AMG-based capitated budgeting could improve equity in healthcare financing across regions in Spain. By explicitly incorporating multimorbidity burden into allocation formulas, resources can be reallocated towards areas with poorer overall population health. Further policy analysis and adjustment is needed before full-scale implementation of such need-based global budgets.

Strengthening primary health care in Europe with digital solutions

This article provides an in-depth analysis of digital transformation in European primary healthcare (PHC). It assesses the impact of digital technology on healthcare delivery and management, highlighting variations in digital maturity across Europe. It emphasizes the significance of digital tools, especially during the COVID-19 pandemic, in enhancing accessibility and efficiency in healthcare. It discusses the integration of telehealth, remote monitoring, and e-health solutions, showcasing their role in patient empowerment and proactive care. Examples are included from various countries, such as Greece's ePrescription system, Lithuania's adoption of remote consultations, Spain's use of risk stratification solutions, and the Netherlands' advanced use of telemonitoring solutions, to illustrate the diverse implementation of digital solutions in PHC. The article offers insights into the challenges and opportunities of embedding digital technologies into a multidisciplinary healthcare framework, pointing towards future directions for PHC in Europe.

Toward Adoption of Health Risk Assessment in Population-Based and Clinical Scenarios: Lessons From JADECARE

Introduction

Health risk assessment (HRA) strategies are cornerstone for health systems transformation toward value-based patient-centred care. However, steps for HRA adoption are undefined. This article analyses the process of transference of the Adjusted Morbidity Groups (AMG) algorithm from the Catalan Good Practice to the Marche region (IT) and to Viljandi Hospital (EE), within the JADECARE initiative (2020-2023).

Description

The implementation research approach involved a twelve-month pre-implementation period to assess feasibility and define the local action plans, followed by a sixteen-month implementation phase. During the two periods, a well-defined combination of experience-based co-design and quality improvement methodologies were applied.

Discussion

The evolution of the Catalan HRA strategy (2010-2023) illustrates its potential for health systems transformation, as well as its transferability. The main barriers and facilitators for HRA adoption were identified. The report proposes a set of key steps to facilitate site customized deployment of HRA contributing to define a roadmap to foster large-scale adoption across Europe.

Conclusions

Successful adoption of the AMG algorithm was achieved in the two sites confirming transferability. Marche identified the key requirements for a population-based HRA strategy, whereas Viljandi Hospital proved its potential for clinical use paving the way toward value-based healthcare strategies.

Healthcare risk stratification model for emergency departments based on drugs, income and comorbidities: the DICER-score

BACKGROUND

During the last decade, the progressive increase in age and associated chronic comorbidities and polypharmacy. However, assessments of the risk of emergency department (ED) revisiting published to date often neglect patients' pharmacotherapy plans, thus overseeing the Drug-related problems (DRP) risks associated with the therapy burden. The aim of this study is to develop a predictive model for ED revisit, hospital admission, and mortality based on patient's characteristics and pharmacotherapy.

METHODS

Retrospective cohort study including adult patients visited in the ED (triage 1, 2, or 3) of multiple hospitals in Catalonia (Spain) during 2019. The primary endpoint was a composite of ED visits, hospital admission, or mortality 30 days after ED discharge. The study population was randomly split into a model development (60%) and validation (40%) datasets. The model included age, sex, income level, comorbidity burden, measured with the Adjusted Morbidity Groups (GMA), and number of medications. Forty-four medication groups, associated with medication-related health problems, were assessed using ATC codes. To assess the performance of the different variables, logistic regression was used to build multivariate models for ED revisits. The models were created using a "stepwise-forward" approach based on the Bayesian Information Criterion (BIC). Area under the curve of the receiving operating characteristics (AUCROC) curve for the primary endpoint was calculated.

RESULTS

851.649 patients were included; 134.560 (15.8%) revisited the ED within 30 days from discharge, 15.2% were hospitalized and 9.1% died within 30 days from discharge. Four factors (sex, age, GMA, and income level) and 30 ATC groups were identified as risk factors and combined into a final score. The model showed an AUCROC values of 0.720 (95%CI:0.718-0.721) in the development cohort and 0.719 (95%CI.0.717-0.721) in the validation cohort. Three risk categories were generated, with the following scores and estimated risks: low risk: 18.3%; intermediate risk: 40.0%; and high risk: 62.6%.

CONCLUSION

The DICER score allows identifying patients at high risk for ED revisit within 30 days based on sociodemographic, clinical, and pharmacotherapeutic characteristics, being a valuable tool to prioritize interventions on discharge.

Economic impact of inflammatory bowel disease in Catalonia: a population-based analysis

Background

Inflammatory bowel disease (IBD) has a major economic impact on healthcare costs.

Objectives

The aim of this study was to evaluate the current healthcare expenditure associated with IBD in a population-wide study in Catalonia.

Design

Retrospective observational study.

Methods

All patients with IBD included in the Catalan Health Surveillance System (CHSS) were considered eligible. The CHSS compiles data on more than 7 million individuals in 2020 (34,823 with IBD). Data on the use of healthcare resources and its economic impact were extracted applying the International Classification of Diseases, 10th revision, Clinical Modification codes (ICD-10-CM codes). Health expenditure, comorbidities, and hospitalization were calculated according to the standard costs of each service provided by the Department of Health of the Catalan government. The data on the IBD population were compared with non-IBD population adjusted for age, sex, and income level. IBD costs were recorded separately for Crohn's disease (CD) and ulcerative colitis (UC).

Results

Prevalence of comorbidities was higher in patients with IBD than in those without. The risk of hospitalization was twice as high in the IBD population. The overall healthcare expenditure on IBD patients amounted to 164M€. The pharmacy cost represents the 60%. The average annual per capita expenditure on IBD patients was more than 3.4-fold higher (IBD 4200€, non-IBD 1200€). Average costs of UC were 3400€ and 5700€ for CD.

Conclusion

The risk of comorbidities was twice as high in patients with IBD and their use of healthcare resources was also higher than that of their non-IBD counterparts. Per capita healthcare expenditure was approximately 3.4 times higher in the population with IBD.

Trial registration

The study was not previously registered.

Five years of Hospital at Home adoption in Catalonia: impact, challenges, and proposals for quality assurance

BACKGROUND

Hospital at home (HaH) was increasingly implemented in Catalonia (7.7 M citizens, Spain) achieving regional adoption within the 2011-2015 Health Plan. This study aimed to assess population-wide HaH outcomes over five years (2015-2019) in a consolidated regional program and provide context-independent recommendations for continuous quality improvement of the service.

METHODS

A mixed-methods approach was adopted, combining population-based retrospective analyses of registry information with qualitative research. HaH (admission avoidance modality) was compared with a conventional hospitalization group using propensity score matching techniques. We evaluated the 12-month period before the admission, the hospitalization, and use of healthcare resources at 30 days after discharge. A panel of experts discussed the results and provided recommendations for monitoring HaH services.

RESULTS

The adoption of HaH steadily increased from 5,185 episodes/year in 2015 to 8,086 episodes/year in 2019 (total episodes 31,901; mean age 73 (SD 17) years; 79% high-risk patients. Mortality rates were similar between HaH and conventional hospitalization within the episode [76 (0.31%) vs. 112 (0.45%)] and at 30-days after discharge [973(3.94%) vs. 1112(3.24%)]. Likewise, the rates of hospital re-admissions at 30 days after discharge were also similar between groups: 2,00 (8.08%) vs. 1,63 (6.58%)] or ER visits [4,11 (16.62%) vs. 3,97 (16.03%). The 27 hospitals assessed showed high variability in patients' age, multimorbidity, severity of episodes, recurrences, and length of stay of HaH episodes. Recommendations aiming at enhancing service delivery were produced.

CONCLUSIONS

Besides confirming safety and value generation of HaH for selected patients, we found that this service is delivered in a case-mix of different scenarios, encouraging hospital-profiled monitoring of the service.

Study protocol of a randomized controlled trial to assess safety of teleconsultation compared with face-to-face consultation: the ECASeT study

BACKGROUND

The use of remote consultation modalities has exponentially grown in the past few years, particularly since the onset of the COVID-19 pandemic. Although a huge body of the literature has described the use of phone (tele) and video consultations, very few of the studies correspond to randomized controlled trials, and none of them has assessed the safety of these consultation modalities as the primary objective. The primary objective of this trial was to assess the safety of remote consultations (both video and teleconsultation) in the follow-up of patients in the hospital setting.

METHODS

Multicenter, randomized controlled trial being conducted in four centers of an administrative healthcare area in Catalonia (North-East Spain). Participants will be screened from all individuals, irrespective of age and sex, who require follow-up in outpatient consultations of any of the departments involved in the study. Eligibility criteria have been established based on the local guidelines for screening patients for remote consultation. Participants will be randomly allocated into one of the two study arms: conventional face-to-face consultation (control) and remote consultation, either teleconsultation or video consultation (intervention). Routine follow-up visits will be scheduled at a frequency determined by the physician based on the diagnostic and therapy of the baseline disease (the one triggering enrollment). The primary outcome will be the number of adverse reactions and complications related to the baseline disease. Secondary outcomes will include non-scheduled visits and hospitalizations, as well as usability features of remote consultations. All data will either be recorded in an electronic clinical report form or retrieved from local electronic health records. Based on the complications and adverse reaction rates reported in the literature, we established a target sample size of 1068 participants per arm. Recruitment started in May 2022 and is expected to end in May 2024.

DISCUSSION

The scarcity of precedents on the assessment of remote consultation modalities using randomized controlled designs challenges making design decisions, including recruitment, selection criteria, and outcome definition, which are discussed in the manuscript.

TRIAL REGISTRATION

NCT05094180. The items of the WHO checklist for trial registration are available in Additional file 1. Registered on 24 November 2021.

The influence of general practitioner and patient sex on the treatment of major depression

Introduction: Biological and sociocultural factors may lead to a significant gender bias in the treatment of major depression and thus contribute to accentuating gender inequalities. However, the influence of the general practitioner's (GP's) sex on the prescription of antidepressants has not been adequately assessed in previous work and remains unclear. This retrospective cohort study aims to determine the influence of GP and patient sex on the treatment of major depression. Methods: The study population comprised 87,629 patients (33.56% male patients and 66.44% female patients) aged over 15 years newly diagnosed with major depression recorded between 2017 and 2019 in Catalonia, Spain. Logistic regression models were used to evaluate the effect of GP sex on the therapeutic strategy (i.e., whether antidepressants were prescribed at the first diagnostic visit). Cox proportional hazards models and survival analyses were conducted to compare, according to GP and patient sex, the probability that a patient would be prescribed an antidepressant at any time during the study period. Finally, a multiple linear regression analysis was performed to assess the pharmacological intensity of the treatment [monthly fluoxetine-equivalent defined daily dose (DDD)]. Results: Female patients were more likely to be prescribed an antidepressant at the time of diagnosis, both by male [OR = 1.11, 95% CI = (1.05, 1.17), p < 0.001] and female GPs [OR = 1.13, 95% CI = (1.09, 1.17), p < 0.001]. Similarly, female patients were 8% and 9% more likely than male patients to be prescribed an antidepressant from male [HR = 1.08, 95% CI = (1.05, 1.11), p < 0.001] and female GPs [HR = 1.09, 95% CI = (0.92, 1.07), p < 0.001], respectively, during the study period. Female GPs prescribed less antidepressants than male GPs: an average of 0.39 less monthly fluoxetine-equivalent DDD [β = -0.39, 95% CI = (0.10, -3.92), p < 0.001]. Discussion: Few differences are observed between male and female GPs regarding the therapeutic strategy and its intensity for the treatment of major depression. However, both male and female GPs are influenced by biases and stereotypes that entail differential antidepressant-prescribing behaviors in accordance with the sex of the patient and their characteristics.

Clinical characterization of patients with anal fistula during follow-up of anorectal abscess: a large population-based study

PURPOSE

Approximately 15-50% of patients with an anorectal abscess will develop an anal fistula, but the true incidence of this entity is currently unknown. The aim of the study was to determine the incidence of anorectal abscess and development of a fistula in a specific population area and to identify potential risk factors associated with demographic, socioeconomic and pre-existing disease (e.g. diabetes and inflammatory bowel disease).

METHODS

A longitudinal observational study was designed including a large cohort study in an area with 7,553,650 inhabitants in Spain 1st january 2014 to 31st december 2019. Adults who attended for the first time with an anorectal abscess and had a minimum of 1-year follow-up were included. The diagnosis was made using ICD-10 codes for anorectal abscess and anal fistula.

RESULTS

During the study period, we included 27,821 patients with anorectal abscess. There was a predominance of men (70%) and an overall incidence of 596 per million population. The overall incidence of anal fistula developing from abscesses was 20%, with predominance in men, and a lower incidence in the lowest income level. The cumulative incidence of fistula was higher in men and in younger patients (p < 0.0001). On multivariate analysis, patients aged 60-69 years (hazard ratio 2.0) and those with inflammatory bowel disease (hazard ratio 1.8-2.0) had a strong association with fistula development (hazard ratio 2.0).

CONCLUSIONS

One in five patients with an anorectal abscess will develop a fistula, with a higher likelihood in men. Fistula formation was strongly associated with inflammatory bowel disease.

Long-term disease interactions amongst surgical patients: a population cohort study

BACKGROUND

The average age of the surgical population continues to increase, as does prevalence of long-term diseases. However, outcomes amongst multi-morbid surgical patients are not well described.

METHODS

We included adults undergoing non-obstetric surgical procedures in the English National Health Service between January 2010 and December 2015. Patients could be included multiple times in sequential 90-day procedure spells. Multi-morbidity was defined as presence of two or more long-term diseases identified using a modified Charlson comorbidity index. The primary outcome was 90-day postoperative death. Secondary outcomes included emergency hospital readmission within 90 days. We calculated age- and sex-adjusted odds ratios (OR) with 95% confidence intervals (CI) using logistic regression. We compared the outcomes associated with different disease combinations.

RESULTS

We identified 20 193 659 procedure spells among 13 062 715 individuals aged 57 (standard deviation 19) yr. Multi-morbidity was present among 2 577 049 (12.8%) spells with 195 965 deaths (7.6%), compared with 17 616 610 (88.2%) spells without multi-morbidity with 163 529 deaths (0.9%). Multi-morbidity was present in 1 902 859/16 946 808 (11.2%) elective spells, with 57 663 deaths (2.7%, OR 4.9 [95% CI: 4.9-4.9]), and 674 190/3 246 851 (20.7%) non-elective spells, with 138 302 deaths (20.5%, OR 3.0 [95% CI: 3.0-3.1]). Emergency readmission followed 547 399 (22.0%) spells with multi-morbidity compared with 1 255 526 (7.2%) without. Multi-morbid patients accounted for 57 663/114 783 (50.2%) deaths after elective spells, and 138 302/244 711 (56.5%) after non-elective spells. The rate of death varied five-fold from lowest to highest risk disease pairs.

CONCLUSION

One in eight patients undergoing surgery have multi-morbidity, accounting for more than half of all postoperative deaths. Disease interactions amongst multi-morbid patients is an important determinant of patient outcome.

A Practice-Proven Adaptive Case Management Approach for Innovative Health Care Services (Health Circuit): Cluster Randomized Clinical Pilot and Descriptive Observational Study

BACKGROUND

Digital health tools may facilitate the continuity of care. Enhancement of digital aid is imperative to prevent information gaps or redundancies, as well as to facilitate support of flexible care plans.

OBJECTIVE

The study presents Health Circuit, an adaptive case management approach that empowers health care professionals and patients to implement personalized evidence-based interventions, thanks to dynamic communication channels and patient-centered service workflows; analyze the health care impact; and determine its usability and acceptability among health care professionals and patients.

METHODS

From September 2019 to March 2020, the health impact, usability (measured with the system usability scale; SUS), and acceptability (measured with the net promoter score; NPS) of an initial prototype of Health Circuit were tested in a cluster randomized clinical pilot (n=100) in patients with high risk for hospitalization (study 1). From July 2020 to July 2021, a premarket pilot study of usability (with the SUS) and acceptability (with the NPS) was conducted among 104 high-risk patients undergoing prehabilitation before major surgery (study 2).

RESULTS

In study 1, Health Circuit resulted in a reduction of emergency room visits (4/7, 13% vs 7/16, 44%), enhanced patients' empowerment (P<.001) and showed good acceptability and usability scores (NPS: 31; SUS: 54/100). In study 2, the NPS was 40 and the SUS was 85/100. The acceptance rate was also high (mean score of 8.4/10).

CONCLUSIONS

Health Circuit showed potential for health care value generation and good acceptability and usability despite being a prototype system, prompting the need for testing a completed system in real-world scenarios.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04056663; https://clinicaltrials.gov/ct2/show/NCT04056663.

Living longer in declining health: Factors driving healthcare costs among older people

BACKGROUND

Developed countries are facing challenges in caring for people who are living longer but with a greater morbidity burden. Such people are likely to be regular users of healthcare.

OBJECTIVES

Our analytical aim is to identify factors that explain healthcare costs among: (1) people over 55 years old; (2) the top 5% and 1% high-cost users among this population; (3) those that transition into the top 5% and 1% from one year to the next; (4) those that appear in the top 5% and 1% over multiple years; and (5) those that remain in the top 5% and 1% over consecutive years.

METHODS

The data covered 2011 to 2017 and comprised 1,485,170 observations for a random sample of 224,249 people aged over 55 years in the Catalan region of Spain. We analysed each person's annual healthcare costs across all public healthcare settings related to their age, gender, socio-economic status (SES), whether or not and when they died, and morbidity status, through Adjusted Morbidity Groups.

RESULTS

After controlling for morbidity status, the oldest people did not have the highest costs and were less likely to be among the most costly patients. There was also only a modest impact on costs associated with SES and with dying. Healthcare costs were substantially higher for those with a neoplasm or four or more long term conditions (LTCs), costs rising with the complexity of their conditions. These morbidity indicators were also the most important factors associated with being and remaining in the top 5% or top 1% of costs.

CONCLUSION

Our results suggest that age and proximity to death are poor predictors of higher costs. Rather, healthcare costs are explained mainly by morbidity status, particularly whether someone has neoplasms or multiple LTCs. Morbidity measures should be included in future studies of healthcare costs.

Defining Multimorbidity in Older Patients Hospitalized with Medical Conditions

BACKGROUND

The term "multimorbidity" identifies high-risk, complex patients and is conventionally defined as ≥2 comorbidities. However, this labels almost all older patients as multimorbid, making this definition less useful for physicians, hospitals, and policymakers.

OBJECTIVE

Develop new medical condition-specific multimorbidity definitions for patients admitted with acute myocardial infarction (AMI), heart failure (HF), and pneumonia patients. We developed three medical condition-specific multimorbidity definitions as the presence of single, double, or triple combinations of comorbidities - called Qualifying Comorbidity Sets (QCSs) - associated with at least doubling the risk of 30-day mortality for AMI and pneumonia, or one-and-a-half times for HF patients, compared to typical patients with these conditions.

DESIGN

Cohort-based matching study PARTICIPANTS: One hundred percent Medicare Fee-for-Service beneficiaries with inpatient admissions between 2016 and 2019 for AMI, HF, and pneumonia.

MAIN MEASURES

Thirty-day all-location mortality KEY RESULTS: We defined multimorbidity as the presence of ≥1 QCS. The new definitions labeled fewer patients as multimorbid with a much higher risk of death compared to the conventional definition (≥2 comorbidities). The proportions of patients labeled as multimorbid using the new definition versus the conventional definition were: for AMI 47% versus 87% (p value<0.0001), HF 53% versus 98% (p value<0.0001), and pneumonia 57% versus 91% (p value<0.0001). Thirty-day mortality was higher among patients with ≥1 QCS compared to ≥2 comorbidities: for AMI 15.0% versus 9.5% (p<0.0001), HF 9.9% versus 7.0% (p <0.0001), and pneumonia 18.4% versus 13.2% (p <0.0001).

CONCLUSION

The presence of ≥2 comorbidities identified almost all patients as multimorbid. In contrast, our new QCS-based definitions selected more specific combinations of comorbidities associated with substantial excess risk in older patients admitted for AMI, HF, and pneumonia. Thus, our new definitions offer a better approach to identifying multimorbid patients, allowing physicians, hospitals, and policymakers to more effectively use such information to consider focused interventions for these vulnerable patients.

Adjusted morbidity groups and survival: a retrospective cohort study of primary care patients with chronic conditions

BACKGROUND

Chronic conditions are one of the main determinants of frailty, functional disability, loss of quality of life and the number one cause of death worldwide. This study aimed to describe the survival of patients with chronic conditions who were followed up in primary care according to the level of risk by adjusted morbidity groups and to analyse the effects of sex, age, clinician and care factors on survival.

METHODS

This was a longitudinal observational study of a retrospective cohort of patients with chronic conditions identified by the adjusted morbidity group stratifier of the electronic medical records in a primary health centre of the Region of Madrid, which has an assigned population of 18,107 inhabitants. The follow-up period was from June 2015 to June 2018. A description of survival according to the Kaplan-Meier method and Cox proportional hazards multivariate regression model was used to analyse the effects of sex, age, clinician and care factors.

RESULTS

A total of 9,866 patients with chronic conditions were identified; 77.4% (7,638) had a low risk, 18.1% (1,784) had a medium risk, and 4.5% (444) had a high risk according to the adjusted morbidity groups. A total of 477 patients with chronic conditions died (4.8%). The median survival was 36 months. The factors associated with lower survival were age over 65 years (hazard ratio [HR] = 1.3; 95% confidence interval [CI] = 1.1-1.6), receiving palliative care (HR = 3.4; 95% CI = 2.6-4.5), high versus low risk level (HR = 2.4; 95% CI = 1.60-3.7), five chronic conditions or more (HR = 1.5; 95% CI = 1.2-2), complexity index (HR = 1.01; 95% CI = 1.02-1.04) and polymedication (HR = 2.6; 95% CI = 2.0-3.3).

CONCLUSIONS

There was a gradual and significant decrease in the survival of patients with chronic conditions according to their level of risk as defined by adjusted morbidity groups. Other factors, such as older age, receiving palliative care, high number of chronic conditions, complexity, and polymedication, had a negative effect on survival. The adjusted morbidity groups are useful in explaining survival outcomes and may be valuable for clinical practice, resource planning and public health research.

Simple approaches to characterising multiple long-term conditions (multimorbidity) and rates of emergency hospital admission: Findings from 495,465 UK Biobank participants

BACKGROUND

Numerous approaches are used to characterise multiple long-term conditions (MLTC), including counts and indices. Few studies have compared approaches within the same dataset. We aimed to characterise MLTC using simple approaches, and compare their prevalence estimates of MLTC and associations with emergency hospital admission in the UK Biobank.

METHODS

We used baseline data from 495,465 participants (age 38-73 years) to characterise MLTC using four approaches: Charlson index (CI), Byles index (BI), count of 43 conditions (CC) and count of body systems affected (BC). We defined MLTC as more than two conditions using CI, BI and CC, and more than two body systems using BC. We categorised scores (incorporating weightings for the indices) from each approach as 0, 1, 2 and 3+. We used linked hospital episode statistics and performed survival analyses to test associations with an endpoint of emergency hospital admission or death over 5 years.

RESULTS

The prevalence of MLTC was 44% (BC), 33% (CC), 6% (BI) and 2% (CI). Higher scores using all approaches were associated with greater outcome rates independent of sex and age group. For example, using CC, compared with score 0, score 2 had 1.95 (95% CI: 1.91, 1.99) and a score of 3+ had 3.12 (95% CI: 3.06, 3.18) times greater outcome rates. The discriminant value of all approaches was modest (C-statistics 0.60-0.63).

CONCLUSIONS

The counts classified a greater proportion as having MLTC than the indices, highlighting that prevalence estimates of MLTC vary depending on the approach. All approaches had strong statistical associations with emergency hospital admission but a modest ability to identify individuals at risk.

Differential Expression of Serum Proteins in Chronic Obstructive Pulmonary Disease Assessed Using Label-Free Proteomics and Bioinformatics Analyses

PURPOSE

As a common respiratory disease, chronic obstructive pulmonary disease (COPD) has a high morbidity and mortality. Current clinical therapies are not ideal and do not improve lung function or long-term life quality. It is very important to find new potential pathogenic mechanisms, biomarkers, and targets with therapeutic value in COPD.

METHODS

Serum samples collected from acute exacerbation and stable COPD and healthy participants were analyzed using label-free liquid chromatography tandem mass spectrometry to identify the differentially expressed proteins (DEPs) between two groups. Bioinformatics analyses were performed to determine the biological processes associated with those DEPs. Key proteins were validated by enzyme linked immunosorbent assay (ELISA).

RESULTS

In total, 661 proteins were detected in serum from patients with COPD and healthy participants. Compared with healthy participants, patients with acute exacerbation of COPD had 45 DEPs, 13 were upregulated and 32 were downregulated; and patients with stable COPD had 79 DEPs, 18 were upregulated and 61 were downregulated. Gene Ontology functional annotation results indicated that the DEPs identified in patients with COPD were associated with the terms cellular anatomical entity, binding, and cellular process. Kyoto Encyclopedia of Genes and Genomes functional annotation analysis and the Clusters of Orthologous Genes database analysis indicated that the functions of these DEPs were primarily in signal transduction mechanisms and amino acid transport and metabolism. The ELISA results for three key proteins of IGFBP2, LRG1 and TAGLN were consistent with the LC-MS/MS results and the area under the receiver operating characteristic of the combined index was 0.893 (95% CI: 0.813, 0.974).

CONCLUSION

Our findings suggested pathogenic mechanisms underlying COPD stages and indicated three key proteins that may warrant further study as potential biomarkers for early diagnosis or prognosis of COPD or as therapeutic targets.

Data and care integration for post-acute intensive care program of stroke patients: effectiveness assessment using a disease-matched comparator cohort

Purpose

To assess the effectiveness of an integrated care program for post-acute care of stroke patients, the return home program (RHP program), deployed in Barcelona (North-East Spain) between 2016 and 2017 in a context of health and social care information systems integration.

Design/methodology/approach

The RHP program was built around an electronic record that integrated health and social care information (with an agreement for coordinated access by all stakeholders) and an operational re-design of the care pathways, which started upon hospital admission instead of discharge. The health outcomes and resource use of the RHP program participants were compared with a population-based matched control group built from central healthcare records of routine care data.

Findings

The study included 92 stroke patients attended within the RHP program and the patients' matched controls. Patients in the intervention group received domiciliary care service, home rehabilitation, and telecare significantly earlier than the matched controls. Within the first two years after the stroke episode, recipients of the RHP program were less frequently institutionalized in a long-term care facility (5 vs 15%). The use of primary care services, non-emergency transport, and telecare services were more frequent in the RHP group.

Originality/value

The authors' analysis shows that an integrated care program can effectively promote and accelerate delivery of key domiciliary care services, reducing institutionalization of stroke patients in the mid-term. The integration of health and social care information allows not only a better coordination among professionals (thus avoiding redundant assessments) but also to monitor health and resource use outcomes of care delivery.

Variability in Healthcare Expenditure According to the Stratification of Adjusted Morbidity Groups in the Canary Islands (Spain)

Morbidity is the main item in the distribution of expenditure on healthcare services. The Adjusted Morbidity Group (AMG) measures comorbidity and complexity and classifies the patient into mutually exclusive clinical categories. The aim of this study is to analyse the variability of healthcare expenditure on users with similar scores classified by the AMG. Observational analytical and retrospective study. Population: 1,691,075 subjects, from Canary Islands (Spain), aged over 15 years with data from health cards, clinical history, Basic Minimum Specialised Healthcare Data Set, AMG, hospital agreements information system and Electronic Prescriptions. A descriptive, bivariant (ANOVA coefficient η²) and multivariant analysis was conducted. There is a correlation between the costs and the weight of AMG (rho = 0.678) and the prescribed active ingredients (rho = 0.689), which is smaller with age and does not exist with the other variables. As for the influence of the AMG morbidity group on the total costs of the patient, the coefficient η² (0.09) obtains a median effect in terms of the variability of expenditure, hence there is intra- and inter-group variability in the cost. In a first model created with all the variables and the cost, an explanatory power of 36.43% (R² = 0.3643) was obtained; a second model that uses solely active ingredients, AMG weight, being female and a pensioner obtained an explanatory power of 36.4%. There is room for improvement in terms of predicting the expenditure.

Development and validation of a population-based risk stratification model for severe COVID-19 in the general population

The shortage of recently approved vaccines against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the need for evidence-based tools to prioritize healthcare resources for people at higher risk of severe coronavirus disease (COVID-19). Although age has been identified as the most important risk factor (particularly for mortality), the contribution of underlying comorbidities is often assessed using a pre-defined list of chronic conditions. Furthermore, the count of individual risk factors has limited applicability to population-based “stratify-and-shield” strategies. We aimed to develop and validate a COVID-19 risk stratification system that allows allocating individuals of the general population into four mutually-exclusive risk categories based on multivariate models for severe COVID-19, a composite of hospital admission, transfer to intensive care unit (ICU), and mortality among the general population. The model was developed using clinical, hospital, and epidemiological data from all individuals among the entire population of Catalonia (North-East Spain; 7.5 million people) who experienced a COVID-19 event (i.e., hospitalization, ICU admission, or death due to COVID-19) between March 1 and September 15, 2020, and validated using an independent dataset of 218,329 individuals with COVID-19 confirmed by reverse transcription—polymerase chain reaction (RT-PCR), who were infected after developing the model. No exclusion criteria were defined. The final model included age, sex, a summary measure of the comorbidity burden, the socioeconomic status, and the presence of specific diagnoses potentially associated with severe COVID-19. The validation showed high discrimination capacity, with an area under the curve of the receiving operating characteristics of 0.85 (95% CI 0.85–0.85) for hospital admissions, 0.86 (0.86–0.97) for ICU transfers, and 0.96 (0.96–0.96) for deaths. Our results provide clinicians and policymakers with an evidence-based tool for prioritizing COVID-19 healthcare resources in other population groups aside from those with higher exposure to SARS-CoV-2 and frontline workers.