Optimización de la predicción de problemas financieros en empresas sanitarias privadas españolas aplicando algoritmos genéticos

ObjectiveThis paper presents a methodology to optimize, using Altman's Z-Score for private companies, the prediction of private companies of the Spanish health sector entering a situation of bankruptcy.MethodThe proposed method consists of the application of genetic algorithms (GA) to find the coefficients of the formula of the chain of ratios proposed by Altman in the version of the score for private companies which optimize the prediction for Spanish private health companies, maximizing sensitivity and specificity, and thereby reducing type I and type II errors. For this purpose, a sample of 5,903 companies from the Spanish private health sector obtained from the database of the Iberian Balance Analysis System (SABI) between 2007 and 2015 was used.ResultsThe results show that the predictive model obtained with the AG presents greater accuracy, sensitivity and specificity than that proposed by Altman for private companies with both test data and all sample data.ConclusionsThe most important finding of this study was to establish a methodology that can identify the optimized coefficients for the Altman Z-Score, which allows a more accurate prediction of bankruptcy in Spanish private healthcare companies.     

Death or Debt? National Estimates of Financial Toxicity in Persons with Newly-Diagnosed Cancer

Purpose

The purpose of this study was to evaluate the impact of cancer upon a patient's net worth and debt in the US.

Medical Bankruptcy in the United States, 2007: Results of a National Study

Background

Our 2001 study in 5 states found that medical problems contributed to at least 46.2% of all bankruptcies. Since then, health costs and the numbers of un- and underinsured have increased, and bankruptcy laws have tightened.

Methods

We surveyed a random national sample of 2314 bankruptcy filers in 2007, abstracted their court records, and interviewed 1032 of them. We designated bankruptcies as “medical” based on debtors' stated reasons for filing, income loss due to illness, and the magnitude of their medical debts.

Results

Using a conservative definition, 62.1% of all bankruptcies in 2007 were medical; 92% of these medical debtors had medical debts over $5000, or 10% of pretax family income. The rest met criteria for medical bankruptcy because they had lost significant income due to illness or mortgaged a home to pay medical bills. Most medical debtors were well educated, owned homes, and had middle-class occupations. Three quarters had health insurance. Using identical definitions in 2001 and 2007, the share of bankruptcies attributable to medical problems rose by 49.6%. In logistic regression analysis controlling for demographic factors, the odds that a bankruptcy had a medical cause was 2.38-fold higher in 2007 than in 2001.

Conclusions

Illness and medical bills contribute to a large and increasing share of US bankruptcies.     

Can a violation of investor trust lead to financial contagion in the market for tax-exempt hospital bonds?

Not-for-profit hospitals rely heavily on tax-exempt debt. Investor confidence in such instruments was shaken by the 1998 bankruptcy of the Allegheny Health and Education Research Foundation (AHERF), which was the largest U.S. not-for-profit failure up to that date and whose default was accompanied by claims of accounting irregularities. Such shocks can result in contagion whereby all hospitals are viewed as riskier. We test for the significance and duration of resulting contagion using an industry-specific model of interest cost determinants. Empirical tests indicate that contagion does occur, resulting in higher interest on new debt issues from other hospitals.