Validez de los grupos de morbilidad ajustados respecto a los clinical risk groups en el ámbito de la atención primaria

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.

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.

Pharmacological treatments and medication-related problems in nursing homes in Catalonia: a multidisciplinary approach

Background: Aging correlates with increased frailty, multi-morbidity, and chronic diseases. Furthermore, treating the aged often entails polypharmacy to achieve optimal disease management, augmenting medication-related problems (MRPs). Few guidelines and tools address the problem of polypharmacy and MRPs, mainly within the institutionalized elderly population. Routine pharmacological review is needed among institutionalized patients. This pharmacological review may improve with a multidisciplinary approach of a collaboration of multiple health professionals. This study aimed to describe institutionalized patients, systematically review their medication plans, and then give recommendations and identify MRPs. Methods: A cross-sectional study was performed using data obtained from patients living in five nursing homes in the northern area of Barcelona, Spain. The inclusion criteria comprised institutionalized patients with public health coverage provided by the Health Department of Catalonia. A detailed description of the clinical characteristics, chronic diseases, pharmacological treatments, recommendations, incomplete data, and MRPs, such as potential drug-drug interactions, therapeutic duplications, contraindications, and drugs deemed inappropriate or of doubtful efficacy, was made. The clinical pharmacologist was the medical doctor specialist who acted as the coordinator of the multidisciplinary team and actively reviewed all the prescribed medications to make recommendations and detect MRPs. Results: A total of 483 patients were included. Patients had a mean age of 86.3 (SD 8.8) years, and 72.0% were female individuals. All patients had at least three health-related problems, with a mean of 17.4 (SD 5.6). All patients, except one, had a minimum of one prescription, with a mean of 8.22 drugs prescribed (SD 3.5) per patient. Recommendations were made for 82.4% of the patients. Of these recommendations, verification of adequate use was made for 69.3% and withdrawal of a drug for 49.5%. Conclusion: This study demonstrates a high prevalence of health-related problems and several prescribed drugs in nursing homes in Catalonia. Many recommendations were made, confirming the increased proportion of polypharmacy, MRPs, and the need for standardized interventions. A multidisciplinary team approach, including general practitioners, geriatric assessments, a clinical pharmacist, and a clinical pharmacologist, should address this problem.

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.

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.

Clinical Risk Group as a predictor of mortality in delirious older adults in the emergency department

BACKGROUND

In older people, chronicity is associated with delirium, which in turn increases the risk of developing poor clinical outcomes like nursing home admission and death. The aim is to determine whether chronicity, as assessed by Clinical Risk Groups (CRG), is an independent predictor of mortality in older adults with delirium seen in the emergency department (ED).

METHODS

Prospective study with 18-month follow-up. Included patients were aged 65 years or older, admitted from 1 January to 31 December 2020, and diagnosed and coded for delirium in the ED of a secondary hospital. Patients were followed for 18 months. A survival analysis was performed using the Kaplan-Meier method and a multivariate Cox proportional hazards model.

RESULTS

The study included 125 patients (56 % men, mean age 81.2 years, standard deviation [SD] 7.5). At baseline, level 0 chronicity was present in 4.7 % of the patients; level 1, 23.4 %; level 2, 32.8 %; and level 3, 39.1 %. By study end, 29.68 % (n = 38) had died. Mean survival in the total sample was 176.6 (standard error 25.8) days. Level 3 chronicity was associated with a significantly higher risk of mortality (hazard ratio 3.41, 95 % confidence interval 1.31-8.96).

CONCLUSIONS

Level 3 chronicity, as assessed by Clinical Risk Groups, is an independent predictor of mortality in older ED patients with delirium. Delirium leads to an increased level of chronicity over the following months.

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study

BACKGROUND

Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection.

OBJECTIVE

The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes.

METHODS

Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles.

RESULTS

Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%).

CONCLUSIONS

The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation.

Multicomponent, high-intensity, and patient-centered care intervention for complex patients in transitional care: SPICA program

Multimorbidity is increasingly present in our environment. Besides, this is accompanied by a deterioration of social and environmental conditions and affects the self-care ability and access to health resources, worsening health outcomes and determining a greater complexity of care. Different multidisciplinary and multicomponent programs have been proposed for the care of complex patients around hospital discharge, and patient-centered coordination models may lead to better results than the traditional ones for this type of patient. However, programs with these characteristics have not been systematically implemented in our country, despite the positive results obtained. Hospital Universitario de Canarias cares for patients from the northern area of Tenerife and La Palma, Spain. In this hospital, a multicomponent and high-intensity care program is carried out by a multidisciplinary team (made up of family doctors and nurses together with social workers) with complex patients in the transition of care (SPICA program). The aim of this program is to guarantee social and family reintegration and improve the continuity of primary healthcare for discharged patients, following the patient-centered clinical method. Implementing multidisciplinary and high-intensity programs would improve clinical outcomes and would be cost-effective. This kind of program is directly related to the current clinical governance directions. In addition, as the SPICA program is integrated into a Family and Community Care Teaching Unit for the training of both specialist doctors and specialist nurses, it becomes a place where the specific methodology of those specialties can be carried out in transitional care. During these 22 years of implementation, its continuous quality management system has allowed it to generate an important learning curve and incorporate constant improvements in its work processes and procedures. Currently, research projects are planned to reevaluate the effectiveness of individualized care plans and the cost-effectiveness of the program.

Situation of the Elderly Living Alone: Morbidity and Services Provided from the Field of Primary Health Care of Gran Canaria

The elderly suffer a greater number of health problems and have greater need for assistance and care. (1) Background: to determine the profile of the elderly who live alone, identified according to the Primary Care Health Record of Gran Canaria, and to analyze the sociodemographic data of the target population and determine the characteristics related to morbidity. (2) Methods: descriptive, prospective, cross-sectional study carried out in the Primary Health Care Management of Gran Canaria. The study population was all adults over 65 years of age living alone. The instrument used was the Drago-Electronic Health Record. Data analysis was carried out using RStudio version 1.1.447 software, and descriptive analysis and inferential analysis were carried out using the Chi-square values, T-test for independent samples, and ANOVA. (3) Results: The sample amounted to 8679 subjects, predominantly female sex (86.14%) and with a mean age of 79.4 years. Of the sample, 6.4% lived alone. Based on the classification by Adjusted Morbidity Groups (AMG), subjects with “moderate complications” predominated at 45.5%. (4) Conclusions: It is necessary to implement this type of stratification tool, which allows interventions to be carried out in elderly people at risk.

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.

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

BACKGROUND

Multimorbidity measures are useful for resource planning, patient selection and prioritization, and factor adjustment in clinical practice, research, and benchmarking. We aimed to compare the explanatory performance of the adjusted morbidity group (GMA) index in predicting relevant healthcare outcomes with that of other quantitative measures of multimorbidity.

METHODS

The performance of multimorbidity measures was retrospectively assessed on anonymized records of the entire adult population of Catalonia (North-East Spain). Five quantitative measures of multimorbidity were added to a baseline model based on age, gender, and socioeconomic status: the Charlson index score, the count of chronic diseases according to three different proposals (i.e., the QOF, HCUP, and Karolinska institute), and the multimorbidity index score of the GMA tool. Outcomes included all-cause death, total and non-scheduled hospitalization, primary care and ER visits, medication use, admission to a skilled nursing facility for intermediate care, and high expenditure (time frame 2017). The analysis was performed on 10 subpopulations: all adults (i.e., aged > 17 years), people aged > 64 years, people aged > 64 years and institutionalized in a nursing home for long-term care, and people with specific diagnoses (e.g., ischemic heart disease, cirrhosis, dementia, diabetes mellitus, heart failure, chronic kidney disease, and chronic obstructive pulmonary disease). The explanatory performance was assessed using the area under the receiving operating curves (AUC-ROC) (main analysis) and three additional statistics (secondary analysis).

RESULTS

The adult population included 6,224,316 individuals. The addition of any of the multimorbidity measures to the baseline model increased the explanatory performance for all outcomes and subpopulations. All measurements performed better in the general adult population. The GMA index had higher performance and consistency across subpopulations than the rest of multimorbidity measures. The Charlson index stood out on explaining mortality, whereas measures based on exhaustive definitions of chronic diagnostic (e.g., HCUP and GMA) performed better than those using predefined lists of diagnostics (e.g., QOF or the Karolinska proposal).

CONCLUSIONS

The addition of multimorbidity measures to models for explaining healthcare outcomes increase the performance. The GMA index has high performance in explaining relevant healthcare outcomes and may be useful for clinical practice, resource planning, and public health research.

Use of primary and hospital care health services by chronic patients according to risk level by adjusted morbidity groups

Background

Patients with chronic diseases have increased needs for assistance and care. The objective of this study was to describe the characteristics and use of primary care (PC) and hospital care (HC) health services by chronic patients according to risk level based on adjusted morbidity groups (AMG) and to analyze the associated factors.

Methods

Cross-sectional descriptive observational study. Patients from a basic health area classified as chronically ill by the AMG classification system of the Madrid PC electronic medical record were included. Sociodemographic, clinical-care characteristics (classified as predisposing factors or need factors) and service utilization variables were collected. Univariate, bivariate and simple linear regression analyses were performed.

Results

The sample consisted of 9866 chronic patients and 8332 (84.4%) used health services. Of these service users, 63% were women, mean age was 55.7 (SD = 20.8), 439 (5.3%) were high risk, 1746 (21.2%) were medium risk, and 6041(73.4%) were low risk. A total of 8226 (98.7%) were PC users, and 4284 (51.4%) were HC users. The average number of annual contacts with PC was 13.9 (SD = 15); the average number of contacts with HC was 4.8 (SD = 6.2). Predisposing factors associated with services utilization at both care levels were: age (B coefficient [BC] = 0.03 and 0.018, 95% CI = 0.017–0.052 and 0.008–0.028, respectively, for PC and HC) and Spanish origin (BC = 0.962 and 3.396, 95% CI = 0.198–1.726 and 2.722–4.070); need factors included: palliative care (BC = 10,492 and 5047; 95% CI = 6457–14,526 and 3098-6995), high risk (BC = 4631 and 2730, 95% CI = 3022–6241 and 1.949–3.512), number of chronic diseases (BC = 1.291 and 0.222, 95% CI = 1.068–1.51 and 0.103–0.341) and neoplasms (BC = 2.989 and 4.309, 95% CI = 1.659–4.319 and 3.629–4.989).

Conclusions

The characteristics and PC and HC service utilization of chronic patients were different and varied according to their AMG risk level. There was greater use of PC services than HC services, although utilization of both levels of care was high. Service use was related to predisposing factors such as age and country of origin and, above all, to need factors such as immobility, high risk, and number and type of chronic diseases that require follow-up and palliative care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07020-z.

Chronic diseases in the geriatric population: morbidity and use of primary care services according to risk level

BACKGROUND

Geriatric patients have significant morbidity and greater needs for care and assistance. The objective of this study was to describe the characteristics, morbidity, and use of services in primary care (PC) of patients with chronic diseases older than 65 years according to their risk level assigned by the adjusted morbidity groups (AMG) and to analyse the factors associated with the use of PC services.

METHODS

This was a cross-sectional descriptive observational study. Patients older than 65 years from a healthcare service area, classified as chronically ill by the AMG classification system of the PC electronic medical record of the Community of Madrid, were included. Sociodemographic, clinical-care, and PC service utilization variables were collected. Univariate, bivariate and multivariate analyses were done.

RESULTS

A total of 3292 chronic patients older than 65 years were identified, of whom 1628 (49.5%) were low risk, 1293 (39.3%) were medium risk and 371 (11.3%) were high risk. Their mean age was 78.1 (SD = 8.1) years and 2167 (65.8%) were women. Their mean number of chronic diseases was 3.8 (SD = 2), 89.4% had multimorbidity and 1550 (47.1%) were polymedicated. The mean number of contacts/year with PC was 19.5 (SD = 18.2) [men: 19.4 (SD = 19.8); women: 19.5 (SD = 17.4)]. The mean number of contacts/year in people over 85 years was 25.2 (SD = 19.6); in people 76-85 years old, it was 22.1 (SD = 20.3); and in people 66-75 years old, it was 14.5 (SD = 13.9). The factors associated with greater use of services were age (B coefficient [BC] = 0.3; 95%CI = 0.2-0.4), high risk level (BC = 1.9; 95%CI =0.4-3.2), weight of complexity (BC = 0.7; 95%CI = 0.5-0.8), and ≥ 4 chronic diseases (BC = 0.7; 95%CI = 0.3-1.1).

CONCLUSIONS

In the geriatric population, we found a high number of patients with chronic diseases and there were three levels of risk by AMG with differences in characteristics, morbidity, and use of PC services. The greatest use of services was by patients with older age, high risk level, greater weight of complexity and ≥ 4 chronic diseases. Further research is needed to develop an intervention model more adapted to the reality of the geriatric population based on risk levels by AMG.

Postoperative mortality after surgery for inflammatory bowel disease in the era of biological agents: A population-based study in Southern Europe

BACKGROUND

Despite the efficacy of biological agents, surgery is still required for a large percentage of patients with inflammatory bowel disease (IBD).

AIMS

To assess the postoperative mortality rates and associated risk factors in IBD patients in a population-based setting in the era of biological agents.

METHODS

This is a population-based longitudinal study including all patients diagnosed with IBD in Catalonia who underwent intestinal resection or colectomy between 2007 and 2016, identified from the Catalan Health Surveillance System database. Logistic regression was used to calculate the adjusted odds ratio for postoperative in-hospital and 30-day mortality. Data for Crohn's disease (CD) and ulcerative colitis (UC) were analysed separately.

RESULTS

A total of 1,660 interventions for CD (69%) and 738 for UC (31%) were performed at 55 centres. In-hospital and 30-day postoperative mortality rates were 2.1% and 2.5% for CD, and 5.4% and 6.4% for UC, respectively. In the multivariate logistic regression analysis, comorbidity was associated with in-hospital and 30-day postoperative mortality in CD and UC, whereas age was only associated with mortality in CD and a non-laparoscopic surgical approach with UC.

CONCLUSIONS

In the era of biologicals, the postoperative mortality rate for IBD depends mostly on co-morbidities and age.

[Chronic diseases in the paediatric population: Comorbidities and use of primary care services]

INTRODUCTION

Adjusted morbidity groups (AMG) are being used in the stratification of chronic patients in Primary Care (PC). The aim of this study was to describe the characteristics, prevalence of comorbidities, and use of PC services by chronic paediatric patients as well as to analyse factors associated with the weight of complexity according to AMG.

PATIENTS AND METHODS

A cross-sectional study conducted on patients <18 years-old from a basic health area, classified as chronic according to the AMG of the Madrid Primary Care computerised clinical records. Sociodemographic and clinical-care variables were collected, as well as the use of services in PC. Univariate, bivariate and linear regression analysis were performed.

RESULTS

A total of 2,961 patients<18 years were included, of whom 423 (15.7%) were identified as chronic, and 408 (96.5%) were low risk patients. Their mean age was 9.5 (SD=4.7) years, and 54.1% were male. The mean of chronic diseases was 1.1 (SD=0.4) and 11.3% had multiple morbidity. The most prevalent diseases were asthma (6.1%), attention deficit hyperactivity disorder (ADHD) (1.8%), and obesity (1.4%). The mean number of visits to the paediatrician was 4.9 (SD=6.3). Age<5 years-old (Coefficient B [CB]=2.6, 95% CI=2.1, 3.1), number of chronic diseases (CB=1.6, 95% CI=1.1; 2.1), and annual contacts with PC (CB=0.1, 95% CI=0.06; 0.11) were associated with greater complexity weight.

CONCLUSIONS

A significant number of patients with chronic diseases were found in the paediatric population. The most prevalent diseases were asthma, ADHD, and obesity. The use of PC services was high. The greatest complexity corresponded to nursing and pre-school age, multiple morbidity, and higher number of contacts with PC.

Study protocol of a quasi-experimental trial to compare two models of home care for older people in the primary setting

BACKGROUND

Preventive home visits are suited for patients with reduced mobility, such as older people. Healthcare needs for older patients are expected to increase due to the extended life expectancy estimated in coming years. The implementation of low-cost, patient-centered methodologies may buffer this rise in health care costs without affecting the quality of service. In order to find the best home care model with less investment, this paper describes a study protocol comparing two models of home care for older people.

METHODS

We describe a quasi-experimental study that compares the outcome of two different home care models already implemented in two primary care centers in Badalona (Barcelona, Spain). The traditional model (control model) is integrated in the sense that is continuous, the same primary care center team looks after its assigned patients both at the center and in preventive home visits. The new functional home care model (study model), consisting of a highly trained team, is specifically designed to meet patient needs and give total attention to preventive home interventions. The study will start and end on the expected dates, June 2018 to October 2020, and include all patients over 65 years old already enrolled in the home care programs of the primary care centers selected. The primary endpoint assessed will be the difference in hospitalization days between patients included in both home care programs. Other variables regarding health status, quality of care and resource utilization will also be compared between the two models.

DISCUSSION

The study in progress will assess whether a functional and highly trained home care team will meet the ever-aging population needs in terms of cost and health outcomes better than a traditional, integrated one. Lessons learned from this pilot study will provide guidelines for a future model of home care based on the IHI Triple Aim: better care, better health, and lower costs.

TRIAL REGISTRATION

Registered in ClinicalTrials.gov (Identifier: NCT03461315; March 12, 2018).

Multimorbidity as a predictor of health service utilization in primary care: a registry-based study of the Catalan population

Background

Multimorbidity is highly relevant for both service commissioning and clinical decision-making. Optimization of variables assessing multimorbidity in order to enhance chronic care management is an unmet need. To this end, we have explored the contribution of multimorbidity to predict use of healthcare resources at community level by comparing the predictive power of four different multimorbidity measures.

Methods

A population health study including all citizens ≥18 years (n = 6,102,595) living in Catalonia (ES) on 31 December 2014 was done using registry data. Primary care service utilization during 2015 was evaluated through four outcome variables: A) Frequent attendants, B) Home care users, C) Social worker users, and, D) Polypharmacy. Prediction of the four outcome variables (A to D) was carried out with and without multimorbidity assessment. We compared the contributions to model fitting of the following multimorbidity measures: i) Charlson index; ii) Number of chronic diseases; iii) Clinical Risk Groups (CRG); and iv) Adjusted Morbidity Groups (GMA).

Results

The discrimination of the models (AUC) increased by including multimorbidity as covariate into the models, namely: A) Frequent attendants (0.771 vs 0.853), B) Home care users (0.862 vs 0.890), C) Social worker users (0.809 vs 0.872), and, D) Polypharmacy (0.835 vs 0.912). GMA showed the highest predictive power for all outcomes except for polypharmacy where it was slightly below than CRG.

Conclusions

We confirmed that multimorbidity assessment enhanced prediction of use of healthcare resources at community level. The Catalan population-based risk assessment tool based on GMA presented the best combination of predictive power and applicability.

[Adjusted morbidity groups: Characteristics and comorbidities in patients with chronic conditions according to their risk level in Primary Care]

Objetivos

Describir las características de los pacientes crónicos según el nivel de riesgo asignado por los grupos de morbilidad ajustados (GMA). Analizar los factores asociados al nivel de riesgo alto y estudiar el efecto de cada uno de ellos.

Diseño

Estudio observacional descriptivo transversal con enfoque analítico.

Emplazamiento

Atención Primaria (AP). Servicio Madrileño de Salud.

Participantes

Población de 18.107 pacientes estratificados por los GMA integrados en la historia clínica electrónica de AP de la Comunidad de Madrid.

Mediciones principales

Variables sociodemográficas, clínico-asistenciales y de uso de servicios. Se realizó análisis univariado, bivariado y multivariante.

Resultados

De los 18.107 pacientes se identificaron 9.866 (54,4%) pacientes crónicos, 444 (4,5%) estratificados como de alto riesgo, 1.784 (18,1%) como de medio riesgo y 7.638 (77,4%) como de bajo riesgo. Los de alto riesgo, comparados con medio y bajo riesgo, tenían una edad media mayor (77,8 [12,9]; 72,1 [12,9]; 50,6 [19,4]), menor porcentaje de mujeres (52,3%, 65%, 61,1%), mayor número de enfermedades crónicas (6,7 [2,4]; 4,3 [1,5]; 1,9 [1,1]), polimedicación (79,1%, 43,3%, 6,2%) y contactos con AP (33,9 [28]; 21,4 [17,3]; 7,9 [9,9]) (p < 0,01). En el multivariante el nivel de riesgo alto se relacionó de manera independiente con la edad > 65 (OR = 1,43; IC 95% = 1,03-1,99), sexo masculino (OR = 3,46; IC 95% = 2,64-4,52), inmovilidad (OR = 6,33; IC 95% = 4,40-9,11), número de enfermedades crónicas (OR = 2,60; IC 95% = 2,41-2,81) (p < 0,01) y número de contactos con AP > 7 (OR = 1,95; IC 95% = 1,36-2,80).

Conclusiones

Más de la mitad de la población fue clasificada por los GMA como crónica, y se estratificó en 3 niveles de riesgo que presentaban diferencias en sexo, edad, deterioro funcional, necesidad de cuidados, morbilidad, complejidad, polifarmacia y contactos con AP. La edad > 65, el sexo masculino, la inmovilidad, el número de enfermedades crónicas y los contactos con AP > 7 fueron los factores asociados al alto riesgo.

[Clinical validation of 2 morbidity groups in the primary care setting]

INTRODUCTION

Adjusted Morbidity Groups (GMAs) and the Clinical Risk Groups (CRGs) are population morbidity based stratification tools which classify patients into mutually exclusive categories.

OBJETIVE

To compare the stratification provided by the GMAs, CRGs and that carried out by the evaluators according to the levels of complexity.

DESIGN

Random sample stratified by morbidity risk.

LOCATION

Catalonia.

PARTICIPANTS

Forty paired general practitioners in the primary care, matched pairs.

INTERVENTIONS

Each pair of evaluators had to review 25 clinical records.

MAIN OUTPUTS

The concordance by evaluators, and between the evaluators and the results obtained by the 2 morbidity tools were evaluated according to the kappa index, sensitivity, specificity, and positive and negative predicted values.

RESULTS

The concordance between general practitioners pairs was around the kappa value 0.75 (mean value=0.67), between the GMA and the evaluators was similar (mean value=0.63), and higher than for the CRG (mean value=0.35). The general practitioners gave a score of 7.5 over 10 to both tools, although for the most complex strata, according to the professionals' assignment, the GMA obtained better scores than the CRGs. The professionals preferred the GMAs over the CRGs. These differences increased with the complexity level of the patients according to clinical criteria. Overall, less than 2% of serious classification errors were found by both groupers.

CONCLUSION

The evaluators considered that both grouping systems classified the studied population satisfactorily, although the GMAs showed a better performance for more complex strata. In addition, the clinical raters preferred the GMAs in most cases.