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

Abstract

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.

Health Outcomes from Home Hospitalization: Multisource Predictive Modeling

“…A unique aspect of this research is that predictors considered in the analyses encompass 3 different categories of variables (Table 2S in Multimedia Appendix 1 ): (1) clinical data and biological information [ 22 - 24 ] extracted from patients’ electronic medical records; (2) additional variables often not considered in the clinical records specifically collected in the research protocol to reflect patients’ functional capacities and health care resources; and (3) information from GMA, the population-based, health-risk assessment tool developed and implemented in Catalonia (ES) [ 15 , 16 , 25 ].…”

“…A key specificity of the study is the use of various data sources to estimate the 2 outcomes, mortality and hospital re-admission, as conventional inpatient care. In addition to classical clinical and biological information obtained from electronic medical records (EMR), we have also considered the inclusion of Catalan population–health risk assessment scoring, known as Adjusted Morbidity Groups (GMA) [ 15 , 16 ], and purposely collected data on patients’ performance and frailty.…”

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

“…Moreover, as this information is used for provider payment purposes, external audits are carried out periodically to ensure the quality and reliability of the data. Hence, the CHSS provides carefully monitored population-based health data on the morbidity and mortality of more than 7.5 million people in Southern Europe, and it has been used previously for several research studies [10][11][12][13] .…”

Use of healthcare resources and drug expenditure before and after treatment of chronic hepatitis C with direct antiviral agents

“…It was reported as F0: no fibrosis, F1: minimal fibrosis, F2: moderate fibrosis, F3: severe fibrosis and F4: cirrhosis; an additional category F5: decompensated cirrhosis was created for cases in which the patient had both F4 fibrosis and (a) cirrhosis reported as "decompensated" in the invoicing system; (b) had received typical pharmacological treatment for decompensated cirrhosis (spironolactone, furosemide, carvedilol, propranolol, lactulose or lactitol) during the year prior to the start of treatment, as defined from the Anatomical Therapeutic Chemical codes specified in Appendix 2; (c) had a reported F4 fibrosis and chronic renal failure; or (d) had any urgent admission due to specific complications of cirrhosis (oesophageal varices with or without haemorrhage, hepatic encephalopathy, spontaneous bacterial peritonitis, hepatorenal syndrome, hepatocellular carcinoma, ascites or portal hypertension) as defined by ICD-9-CM codes specified in Appendix 1. Individual morbidity burden [25][26][27] was calculated using a population-based health risk assessment tool deployed in Catalonia, the Adjusted Morbidity Grouper (GMA), which is used to calculate an individual's morbidity burden. GMA categorizes each patient in a risk-stratification pyramid with four strata:…”