Predictive risk modelling in the Spanish population: a cross-sectional study

Abstract

BackgroundAn increase in chronic conditions is currently the greatest threat to human health and to the sustainability of health systems. Risk adjustment systems may enable population stratification programmes to be developed and become instrumental in implementing new models of care.The objectives of this study are to evaluate the capability of ACG-PM, DCG-HCC and CRG-based models to predict healthcare costs and identify patients that will be high consumers and to analyse changes to predictive capacity when socio-economic variables are added.MethodsThis cross-sectional study used data of all Basque Country citizens over 14 years of age (n = 1,964,337) collected in a period of 2 years. Data from the first 12 months (age, sex, area deprivation index, diagnoses, procedures, prescriptions and previous cost) were used to construct the explanatory variables. The ability of models to predict healthcare costs in the following 12 months was assessed using the coefficient of determination and to identify the patients with highest costs by means of receiver operating characteristic (ROC) curve analysis.ResultsThe coefficients of determination ranged from 0.18 to 0.21 for diagnosis-based models, 0.17-0.18 for prescription-based and 0.21-0.24 for the combination of both. The observed area under the ROC curve was 0.78-0.86 (identifying patients with a cost higher than P-95) and 0.83-0.90 (P-99). The values of the DCG-HCC models are slightly higher and those of the CRG models are lower, although prescription information could not be used in the latter. On adding previous cost data, differences between the three systems decrease appreciably. Inclusion of the deprivation index led to only marginal improvements in explanatory power.ConclusionThe case-mix systems developed in the USA can be useful in a publicly financed healthcare system with universal coverage to identify people at risk of high health resource consumption and whose situation is potentially preventable through proactive interventions.

Multimorbidity in risk stratification tools to predict negative outcomes in adult population

“…These tools have been validated in several contexts [20,32,41], and have been even compared among themselves [13,14,31] in the articles reviewed.…”

“…After separating the articles by type of outcome, it has included 1 article which predicts length of stay [8], 3 articles which predict disability [9][10][11], 3 articles which predict healthcare costs [12][13][14] and 29 articles which predict hospital admissions and/or readmissions .…”

Impact of stratification on the effectiveness of a comprehensive patient‐centered strategy for multimorbid patients

“…Higher risk predicted greater costs for the health care system. The process of obtaining the risk score is explained extensively elsewhere . However, the stratification was carried out in a specific moment and was based on data collected during a specific time range, while the evolution of the population was dynamic.…”

“…In particular, this patient‐centered strategy consisted in a risk stratification for case finding (RSCF) strategy launched in 2011 (available from the late 2012 and onward) by the Department of Health. Its aim was to prioritize complex frail and high‐risk patients on the basis of adjusted clinical groups (ACGs) . Patient prioritization implied the activation of a specific care pathway.…”

Global Health and Visa Policy Reform to Address Dangers of Hajj during Summer Seasons

“…The risk modeling tool [we note that predictive modeling has been used for several health challenges including in the acute care setting, for population health, mental health, to model infectious disease risk, and for assessing the potential impact of climate change on health (17–21)] could generate evidence for use in national surveillance systems and inform the committee’s guidance and technical assistance process. This process would specifically focus on proactively assessing existing and emerging health risks associated with extreme temperatures during the Hajj that are likely to be impacted by climate change.…”