Using SAS Predictive Modeling to Investigate the Asthma’s Patient

Future hospitalization Risk.Yehia H. Khalil, University of Louisville, Louisville, KY,US

presented by:

XxxxxxxDSCI 5240

Aim

• Develop a predictive model to forecast future Asthma hospitalization

Asthma

• A chronic inflammatory disorder of the airways

• 21 million Americans diagnosed

• Hospitalization rate growing (more than a million cases a year)

• Costs for Asthma: $14 billion

Predictive modeling

• Ability to incorporate any type of variable into analysis

• Dynamic; can easily accommodate any information to adjust model

SAS SEMMA methodology

• Sample

• Explore

• Modify

• Model

• Access

Source of 2009 Dataset

• Medical Expenditure Panel Survey

• California Health Interview Survey

Survey

• 47,614 adults

• 3,379 adolescents

• 8,945 children

Useful Parameters

• Demographics: age, race, marital status

• Health Behaviors: physical activities, fast food, alcohol consumption

• Health Conditions other than Asthma

• Health Insurance

• Poverty Level

• Emergency preparedness module: medication

• Mental or Emotional Condition

Fig. 4 Analysis Diagram

note:

• 40% training

• 30% testing

• 30% validation

Conclusion

• General health conditions, psychological distress and poverty level

affect future hospitalization risk

• Rx coverage and patient disability influence taking medication

regularly and can increase future hospitalization risk

• It is possible to enhance interventions, programs and alternatives to

avoid future hospitalizations