The Performance of Risk Adjustment Models in Colombian Competitive Health Insurance Market

Alvaro Riascos (ariascos@uniandes.edu.co), Eduardo Alfonso (edalfon@gmail.com) and Mauricio Romero

No 12062, Documentos CEDE from Universidad de los Andes, Facultad de Economía, CEDE

Abstract: We introduce new risk groups to a standard capitation formula and evaluate risk selection incentives of insurers. The study uses a unique data set of almost 24 million affiliates to Government’s mandatory health insurance system. This data set is very rich in the sense of reporting all claims during year 2010, basic demographic variables, initial diagnostic, health services, pharmaceuticals used, etc. It compromises more than 300 million claims. We construct two diagnostic related groups: an adaptation of the 3M algorithm, and a ad hoc diagnostic related group constructed by the authors. Using standard linear capitations formulas we evaluate incentives for cream skimming using several measures. In general, results show a notable improvement in the explanatory power of health expenditures by introducing the ad hoc diagnostic related groups to the standard Colombian risk adjustment formula. With the new risk groups the R2 of the model is 13.53% as opposed to 1.45% of the current formula. Furthermore, for users in the highest expenditure quintile, expected expenditure is 71% of actual expenditure, as opposed to 27% under the current formula. This suggest there is much space for improving the current Colombian capitation formula using information that is currently available.

Keywords: Risk adjustment; Diagnostic Related Groups; Risk Selection. (search for similar items in EconPapers)
JEL-codes: I11 I13 I18 (search for similar items in EconPapers)
Pages: 20
Date: 2014-08-15
New Economics Papers: this item is included in nep-ger, nep-hea and nep-ias
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:col:000089:012062

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