Mortgage debt as a moderator in the association between unemployment and health.

OBJECTIVE: While homeownership is generally viewed as good for society, the consequences of the concomitant mortgage debt have not been well examined. This study investigates the role of mortgage debt as a moderator in the relationship between unemployment and health. STUDY DESIGN: A cross-sectional analysis of a representative sample of US homeowners aged 38-46 surveyed in 1998-2006. METHODS: Subjects were 3667 adults living in owned homes aged 38-46 who reported being either employed or unemployed. Logistic models were performed using maximum likelihood estimation to estimate the relative risk of self-reporting fair or poor health with regard to employment status and how employment status interacted with mortgage status. RESULTS: Among homeowners, being unemployed for more than 13 weeks with a mortgage is associated with a higher likelihood of reporting fair or poor health (odds ratio 2.38, 95% confidence interval 1.28-4.45). Being unemployed for more than 13 weeks with a mortgage loan that is more than 80% of the value of the home is associated with a greater likelihood of reporting fair or poor health (odds ratio 8.99, 95% confidence interval 2.50-32.29). CONCLUSION: Among homeowners, mortgage debt increases the association between unemployment and poor health. In an economy where periods of high unemployment are likely to coincide with periods of falling home prices, homeowners may find themselves unemployed just when their homes lose value, intensifying financial stress.

Monitor: an expert system that validates and interprets time-dependent partial data based on a cystic fibrosis home monitoring program.

The use of health diaries to monitor patients with chronic diseases has often been complicated by difficulties encountered in data quality assurance and interpretation. An expert system, Monitor, has been developed to predict the health status of cystic fibrosis patients based on daily home measurements of pulse, respiratory rate, weight, inspired vital capacity, and a check list of symptoms of acute illness. This system ensures data reliability beyond what can be achieved in most current automatic error detection procedures by validating inputs against patient-specific expectations. Its explicit representation of the time dimension and the hierarchical structure of its knowledge base facilitate the abstraction of trends and relationships among the time-dependent data. Dynamically imposed expectations also lend flexibility to the interpretation process by allowing the processing of partial (incomplete) data. Monitor correctly classified 86 percent (three-category classification) and 94 percent (two-category classification) of 111 cases. This demonstrates that expert systems can be a feasible approach in building more robust diary monitoring systems.