Abstract 17123: Hierarchical Clustering in 2014 Medical Expenditure Panel Survey (MEPS) to Examine the Cost of Events Related to Acute Myocardial Infraction (AMI) and Hypertension (HTN)

Introduction: Every year more than 75 million adults are diagnosed with HTN in the US. Despite spending $50 billion/ year to lower mortality related to HTN, only about 54% of patients have this condition managed. In 2014, mortality due to HTN or related complications was 410,000. AMI is a more severe manifestation of coronary artery disease with 735,000 people diagnosed yearly. It is estimated that hospitals lose $4493-$7940 per patient due to AMI. We studied costs for events associated with HTN and AMI in MEPS. Methods: Individuals aged ≥20 in MEPS (2014) were included. Hierarchical clustering was used for analysis. Age, sex, education status, race, Hispanic ethnicity, US citizenship status, family income, insurance, people who reported HTN and/ or AMI events were entered as dimensions. Cluster and descriptive analyses were adjusted for survey weights. Results: About 236 million weighted individuals were eligible for the analysis (Female: 51.7%; White: 79.1%; High school or above: 61%; Any private insurance: 66%; and Income≥ 400% of poverty line: 41%). Out of these, about 4.9 million participants reported costs/ events related to AMI and 61.8 million related to HTN. Five groups were identified based on similarity within each cluster (Table 1). The maximum number of participants were present in cluster 1 (weighted n = 101 million) with the most number of participants (21.5%) who reported HTN among all cluster groups. In general, cluster 5 had the lowest annual total of direct health care payments and events. Selected medical history is described in Table 2. Conclusion: Results show insights into the cost of HTN and AMI events, and their relationship with other comorbid conditions. Being computationally demanding, clustering methodologies are rarely utilized in survey data analysis. This could be the first example of clustering approach applied to data from MEPS. Once clustering methods are improved in weighted survey data, estimates will be more reliable.