[Adjusted morbidity groups: A new multiple morbidity measurement of use in Primary Care].
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ABSTRACT:
SUBMITTER: Monterde D
PROVIDER: S-EPMC6879171 | biostudies-literature | 2016 Dec
REPOSITORIES: biostudies-literature
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[Adjusted morbidity groups: A new multiple morbidity measurement of use in Primary Care].
Monterde David D Vela Emili E Clèries Montse M
Atencion primaria 20160803 10
The Adjusted Morbidity Groups (GMA) is a new morbidity measurement developed and adapted to the Spanish healthcare System. It enables the population to be classified into 6 morbidity groups, and in turn divided into 5 levels of complexity, along with one healthy population group. Consequently, the population is divided into 31 mutually exclusive categories. The results of the stratification in Catalonia are presented. GMA is a method for grouping morbidity that is comparable to others in the fie ...[more]
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Project description:BackgroundIntracerebral haemorrhage rates are increasing among highly complex, elderly patients. The main objective of this study was to identify modifiable risk factors of intracerebral haemorrhage.MethodsMulticentre, retrospective, community-based cohort study was conducted, including patients in the Adjusted Morbidity Group 4 with no history of intracerebral haemorrhage. Cases were obtained from electronic clinical records of the Catalan Institute of Health and were followed up for five years. The primary outcome was the occurrence of intracerebral haemorrhage during the study period. Demographic, clinical and pharmacological variables were included. Logistic regression analyses were carried out to detect prognostic variables for intracerebral haemorrhage.Results4686 subjects were included; 170 (3.6%) suffered an intracerebral haemorrhage (85.8/10,000 person-year [95% CI 85.4 to 86.2]). The HAS-BLED score for intracerebral haemorrhage risk detection obtained the best AUC (0.7) when used in the highest complexity level (cut-off point ≥3). Associated independent risk factors were age ≥80 years, high complexity and use of antiplatelet agents.ConclusionsThe Adjusted Morbidity Group 4 is associated with a high risk of intracerebral haemorrhage, particularly for highly complex patients and the use of antiplatelet agents. The risk of bleeding in these patients must be closely monitored.
| S-EPMC8702076 | biostudies-literature
Project description:BackgroundPatients 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.MethodsCross-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.ResultsThe 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).ConclusionsThe 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.
| S-EPMC8487403 | biostudies-literature
Project description:AimsTo describe the characteristics of patients with chronic conditions according to their risk levels assigned by the adjusted morbidity groups (AMG). To analyse the factors associated with a high risk level and to study their effect.DesignObservational cross-sectional study with an analytical focus.LocationPrimary care (PC), Madrid Health Service.ParticipantsPopulation of 18,107 patients stratified by their risk levels with the AMG in the computerised clinical records of Madrid PC.Main measurementsThe variables studied were: socio-demographic, clinical-nursing care and use of services. Univariate, bivariate, and multivariate analysis were performed.ResultsOf the 18,107 patients, 9,866(54.4%) were identified as chronic patients, with 444 (4.5%) stratified as high risk, 1784 (18,1%) as medium risk, and 7,638 (77.4%) as low risk. The high risk patients, compared with medium and low risk, had an older mean age [77.8 (SD=12.9), 72.1 (SD=12.9), 50.6 (SD=19.4)], lower percentage of women (52.3%, 65%, 61.1%), a higher number of chronic diseases [6.7 (SD=2.4), 4.3 (SD=1.5), 1.9 (SD=1.1)], polymedication (79.1%, 43.3%, 6.2%), and contact with PC [33.9 (28), 21.4 (17.3), 7.9 (9.9)] (P<.01). In the multivariate analysis, the high risk level was independently related to age>65 [1.43 (1.03-1.99), male gender (OR=3.46, 95% CI=2.64-4.52), immobility (OR=6.33, 95% CI=4.40-9.11), number of chronic conditions (OR=2.60, 95% CI=2.41-2.81), and PC contact>7 times (OR=1.95, 95% CI=1.36-2.80)] (P<.01).ConclusionsMore than half of the population is classified by the AMG as a chronic, and it is stratified into 3 risk levels that show differences in gender, age, functional impairment, need for care, morbidity, complexity, and use of Primary Care services. Age>65, male gender, immobility, number of chronic conditions, and contact with PC>7 times were the factors associated with high risk.
| S-EPMC7025976 | biostudies-literature
Project description:BackgroundIn-hospital care of chronic patients is based on their characteristics and risk levels. Adjusted morbidity groups (AMG) is a population stratification tool which is currently being used in Primary Care but not in Hospitals. The objectives of this study were to describe the use of hospital services by chronic patients according to their risk levels assigned by AMG and to analyze influencing variables.Material and methodsIn this cross-sectional study, patients aged ≥18 years from a healthcare service area classified as chronically ill by the AMG classification system who used their referral hospital services from June 2015 to June 2016 were included. Predisposing and needs factors were collected. Univariate, bivariate and multiple linear regressions were performed.ResultsOf the 9,443 chronic patients identified (52.1% of the population in the selected area), 4,143 (43.9%) used hospital care services. Their mean age was 62.1 years (standard deviation (SD) = 18.4); 61.8% were female; 9% were high risk; 30% were medium risk, and 61% were low risk. The mean number of hospital service contacts was 5.0 (SD = 6.2), with 3.8 (SD = 4.3) visits to outpatient clinic, 0.7 (SD = 1.2) visits to emergency departments, 0.3 (SD = 2.8) visits to day hospital, and 0.2 (SD = 0.5) hospitalizations. The factors associated with greater service use were predisposing factors such as age (coefficient B (CB) = 0.03; 95% confidence interval (CI) = 0.01-0.05) and Spanish origin (CB = 3.9; 95% CI = 3.2-4.6). Among the needs factors were palliative care (CB = 4.8; 95% CI = 2.8-6.7), primary caregiver status (CB = 2.3; 95% CI = 0.7-3.9), a high risk level (CB = 2.9; 95% CI = 2.1-3.6), multimorbidity (CB = 0.8, 95% CI = 0.4-1.3), chronic obstructive pulmonary disease (COPD) (CB = 1.5, 95% CI = 0.8-2.3), depression (CB = 0.8, 95% CI = 0.3-1.3), active cancer (CB = 4.4, 95% CI = 3.7-5.1), and polymedication (CB = 1.1, 95% CI = 0.5-1.7).ConclusionsThe use of hospital services by chronic patients was high and increased with the risk level assigned by the AMG. The most frequent type of contact was outpatient consultation. Use was increased with predisposing factors such as age and geographic origin and by needs factors such as multimorbidity, risk level and severe diseases requiring follow-up, home care, and palliative care.
| S-EPMC8812854 | biostudies-literature
Project description:IntroductionAdjusted Morbidity Groups (GMAs) and the Clinical Risk Groups (CRGs) are population morbidity based stratification tools which classify patients into mutually exclusive categories.ObjetiveTo compare the stratification provided by the GMAs, CRGs and that carried out by the evaluators according to the levels of complexity.DesignRandom sample stratified by morbidity risk.LocationCatalonia.ParticipantsForty paired general practitioners in the primary care, matched pairs.InterventionsEach pair of evaluators had to review 25 clinical records.Main outputsThe 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.ResultsThe 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.ConclusionThe 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.
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Project description:BackgroundWithin the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners' (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP healthcare utilization.MethodsTo reach our aim, two variables of the ACG System, the Aggregated Diagnosis Groups (ADG) and the mutually exclusive ACG categories were explored. The population for this pilot analysis consisted of 23,618 persons listed with five participating general practices within one region in the Netherlands. ACG analyses were performed based on historical Electronic Health Records data from 2014 consisting of primary care diagnoses and pharmaceutical data. Logistic regression models were estimated and AUC's were calculated to explore the diagnostic value of the models including ACGs and ADGs separately with GP healthcare utilization as the dependent variable. The dependent variable was categorized using four different cut-off points: zero, one, two and three visits per year.ResultsThe ACG and ADG models performed as well as models using International Classification of Primary Care chapters, regarding the association with GP utilization. AUC values were between 0.79 and 0.85. These models performed better than the base model (age and gender only) which showed AUC values between 0.64 and 0.71.ConclusionThe results of this study show that the ACG system is a useful tool to stratify Dutch primary care populations with GP healthcare utilization as the outcome variable.
| S-EPMC7945308 | biostudies-literature
Project description:Many wish to change incentives for primary care practices through bundled population-based payments and substantial performance feedback and bonus payments. Recognizing patient differences in costs and outcomes is crucial, but customized risk adjustment for such purposes is underdeveloped.Using MarketScan's claims-based data on 17.4 million commercially insured lives, we modeled bundled payment to support expected primary care activity levels (PCAL) and 9 patient outcomes for performance assessment. We evaluated models using 457,000 people assigned to 436 primary care physician panels, and among 13,000 people in a distinct multipayer medical home implementation with commercially insured, Medicare, and Medicaid patients.Each outcome is separately predicted from age, sex, and diagnoses. We define the PCAL outcome as a subset of all costs that proxies the bundled payment needed for comprehensive primary care. Other expected outcomes are used to establish targets against which actual performance can be fairly judged. We evaluate model performance using R(2)'s at patient and practice levels, and within policy-relevant subgroups.The PCAL model explains 67% of variation in its outcome, performing well across diverse patient ages, payers, plan types, and provider specialties; it explains 72% of practice-level variation. In 9 performance measures, the outcome-specific models explain 17%-86% of variation at the practice level, often substantially outperforming a generic score like the one used for full capitation payments in Medicare: for example, with grouped R(2)'s of 47% versus 5% for predicting "prescriptions for antibiotics of concern."Existing data can support the risk-adjusted bundled payment calculations and performance assessments needed to encourage desired transformations in primary care.
| S-EPMC3394905 | biostudies-literature
Project description:ObjectiveThe burden of chronic disease is not evenly shared within our society. In this manuscript, we use comprehensive national-level data to compare morbidity burden between ethnic groups in New Zealand.MethodsWe investigated the prevalence of morbidity among all New Zealanders aged 18+ (n = 3,296,837), stratified by ethnic group (Māori, Pacific, Asian, Middle Eastern/Latin American/African, European/Other), using national-level hospitalisation and pharmaceutical data and two measures of morbidity (the M3 and P3 indices).Results and conclusionsWe observed substantial disparities for Māori and Pacific peoples compared to other ethnic groups for the vast majority of commonly-diagnosed morbidities. These disparities appeared strongest for the most-common conditions - meaning that Māori and Pacific peoples disproportionately shoulder an increased burden of these key conditions. We also observed that prevalence of these conditions emerged at earlier ages, meaning that Māori and Pacific peoples also experience a disproportionate impact of individual conditions on the quality and quantity of life. Finally, we observed strong disparities in the prevalence of conditions that may exacerbate the impact of COVID-19, such as chronic pulmonary, liver or renal disease. The substantial inequities we have presented here have been created and perpetuated by the social determinants of health, including institutionalised racism: thus solutions will require addressing these systemic issues as well as addressing inequities in individual-level care.
| S-EPMC7658519 | biostudies-literature
Project description:BackgroundOpioid use disorder (OUD) is a deadly illness that remains undertreated, despite effective pharmacological treatments. Barriers, such as stigma, treatment affordability, and a lack of training and prescribing within medical practices result in low access to treatment. Software-delivered measurement-based care (MBC) is one way to increase treatment access. MBC uses systematic patient symptom assessments to inform an algorithm to support clinicians at critical decision points.MethodFocus groups of faculty clinicians (N = 33) from 3 clinics were conducted to understand perceptions of OUD diagnosis and treatment and whether a computerized MBC model might assist with diagnosis and treatment. Themes from the transcribed focus groups were identified in two phases: (1) content analysis focused on uncovering general themes; and (2) systematic coding and interpretation of the data.ResultsAnalysis revealed six major themes utilized to develop the coding terms: "distinguishing between chronic pain and OUD," "current practices with patients using prescribed or illicit opioids or other drugs," "attitudes and mindsets about providing screening or treatment for OUD in your practice," "perceived resources needed for treating OUD," "primary care physician role in patient care not specific to OUD," and "reactions to implementation of proposed clinical decision support tool."ConclusionResults revealed that systemic and attitudinal barriers to screening, diagnosing, and treating OUD continue to persist. Providers tended to view the software-based MBC program favorably, indicating that it may be a solution to increasing accessibility to OUD treatment; however, further interventions to combat stigma would likely be needed prior to implementation of these programs.Trial registrationClinicalTrials.gov; NCT04059016; 16 August 2019; retrospectively registered; https://clinicaltrials.gov/ct2/show/NCT04059016 .
| S-EPMC10598938 | biostudies-literature
Project description:BackgroundGeriatric 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.MethodsThis 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.ResultsA 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).ConclusionsIn 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.
| S-EPMC8074273 | biostudies-literature