Clinical Risk Groups (CRGs)

Hughes, John S.; Averill, Robin; Eisenhandler, Jon; Goldfield, Norbert; Muldoon, John; Neff, John M.; Gay, James C.

doi:10.1097/01.mlr.0000102367.93252.70

Cited by 239 publications

(101 citation statements)

References 2 publications

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“…38 Chronic conditions are defined as physical, mental, emotional, behavioral or developmental disorders, expected to last at least 12 months, or having sequelae that last at least 12 months, and that require ongoing treatment and/or monitoring. All CRGs can be folded into 9 CRG statuses.…”

Section: Methodsmentioning

confidence: 99%

Effect of Gestational Age and Birth Weight on the Risk of Strabismus Among Premature Infants

et al. 2014

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IMPORTANCE Strabismus causes irreversible vision loss if not detected and treated early. It is unclear whether birth weight (BW) and gestational age (GA) are risk factors for strabismus.OBJECTIVE To estimate the effect of BW and GA on the likelihood of premature infants developing strabismus. DESIGN, SETTING, AND PARTICIPANTSIn this longitudinal cohort analysis, we monitored a group of premature children from birth to determine the proportion that developed strabismus and the timing of the first strabismus diagnosis. Multivariable Cox regression analyses assessed the relationships of BW and GA with the development of strabismus. Regression models were adjusted for other risk factors for strabismus, sociodemographic factors, and ocular comorbidities. The analysis included 38 055 otherwise healthy children born prematurely who were enrolled for more than 6 months in a nationwide US managed care network between 2001 and 2011 in communities throughout the United States.EXPOSURES Birth weight less than 2000 g or GA of 32 weeks or less. MAIN OUTCOMES AND MEASURESHazard ratios (HRs) for strabismus with 95% CIs. RESULTSOf 38 055 otherwise healthy children who were born prematurely, 583 received a diagnosis of strabismus later in life. The cumulative incidence of strabismus was 3.0% at 5 years. Controlling for GA and other covariates, infants born with BW less than 2000 g had a 61% increased hazard (HR, 1.61; 95% CI, 1.22-2.13) of developing strabismus. Controlling for BW and other covariates, there was no significant association between strabismus and GA (HR, 0.98; 95% CI, 0.69-1.38). Among premature infants with BW of less than 2000 g, a GA of 32 weeks or less conveyed no additional increased risk for developing strabismus relative to infants born after 32 weeks (HR, 1.27; 95% CI, 0.86-1.88). In contrast, among infants with a GA of 32 weeks or less, BW of less than 2000 g conveyed a 14-fold increase in the risk of strabismus relative to BW of 2000 g or more (HR, 14.39; 95% CI,.CONCLUSIONS AND RELEVANCE Independent of GA, very low BW conferred a large increase in strabismus risk among premature infants. In contrast, independent of BW, GA did not significantly affect the risk of strabismus. Updates to existing guidelines in the pediatric and ophthalmic literature should be considered, highlighting the importance of BW rather than GA and alerting clinicians about the need for careful monitoring of premature infants with low BW for strabismus.

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Section: Methodsmentioning

confidence: 99%

Effect of Gestational Age and Birth Weight on the Risk of Strabismus Among Premature Infants

et al. 2014

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“…1 Likewise, Hughes et al found that in 1998, Medicare's sickest 15.3% of patients accounted for 75.7% of expenditure. 2 In the United Kingdom, the Department of Health reports that around 5% of patients account for 40% of inpatient bed-days. 3 South Auckland Health 4 reports that in 2001, 14% of patients accounted for 44% of adult medical bed-days (national data are not available in New Zealand).…”

Section: Introductionmentioning

confidence: 99%

Predictive risk modelling in health: options for New Zealand and Australia

Panattoni¹,

Vaithianathan

Ashton³

et al. 2011

Aust. Health Review

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Abstract.Predictive risk models (PRMs) are case-finding tools that enable health care systems to identify patients at risk of expensive and potentially avoidable events such as emergency hospitalisation. Examples include the PARR (Patients-atRisk-of-Rehospitalisation) tool and Combined Predictive Model used by the National Health Service in England. When such models are coupled with an appropriate preventive intervention designed to avert the adverse event, they represent a useful strategy for improving the cost-effectiveness of preventive health care. This article reviews the current knowledge about PRMs and explores some of the issues surrounding the potential introduction of a PRM to a public health system. We make a particular case for New Zealand, but also consider issues that are relevant to Australia.

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“…CRG classifies people into one of the following health states: 1) Healthy; 2) Significant acute illness; 3) Single or multiple minor chronic disease; 4) Moderate chronic diseases; 5) Chronic dominant diseases; 6–7) Multiple dominant chronic diseases; 8) Advanced neoplastic disease; 9) Catastrophic diseases. Its purpose, among others, is to detect diseases requiring greater attention, to monitor prevalence rates of chronic diseases, to understand the patterns of use and consumption of services and to develop risk and price adjustment applications [25]. …”

Section: Theory and Methodsmentioning

confidence: 99%

Complex Care Needs in Multiple Chronic Conditions: Population Prevalence and Characterization in Primary Care. A Study Protocol

Iglesias¹,

Celada²,

Navarro³

et al. 2018

International Journal of Integrated Care

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Background:Chronicity, and particularly complex care needs for people with chronic diseases is one of the main challenges of health systems.Objective:To determine the population prevalence of people with chronic diseases and complex care needs and to characterize these needs considering features of health and social complexity in Primary Care.Design:Cross-sectional population-based study.Scope:Patients who have one or more chronic health conditions from three Primary Care urban centres of a reference population of 43.647 inhabitants older than 14 years old.Methodology:Data will be obtained from the review of electronical medical records. Complexity will be defined by: 1) the independent clinical judgment of primary care physicians and nurses and 2) the aid of three complexity domains (clinical and social). Patients with advanced chronic disease and limited life prognosis will be also described.Conclusions:This research protocol intends to describe and analyse complex care needs from a primary care professional perspective in order to improve knowledge of complexity beyond multimorbidity and previous consumption of health resources. Knowing about health and social complexity with a more robust empirical basis could help for a better integration of social and health policies and a more proactive and differentiated care approach in this most vulnerable population.

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Clinical Risk Groups (CRGs)

Abstract: CRGs performance is comparable to other risk adjustment systems. CRGs have the potential to provide risk adjustment for capitated payment systems and management systems that support care pathways and case management.

Cited by 239 publications

References 2 publications

Effect of Gestational Age and Birth Weight on the Risk of Strabismus Among Premature Infants

Effect of Gestational Age and Birth Weight on the Risk of Strabismus Among Premature Infants

Predictive risk modelling in health: options for New Zealand and Australia

Complex Care Needs in Multiple Chronic Conditions: Population Prevalence and Characterization in Primary Care. A Study Protocol

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