Patients As Reporters Of Health Care Utilization: An Analysis Of All- Cause And Disease-Specific Patient-Reported Health Care Utilization Compared To Administrative Claims Data

Abstract

who did not, and (2) between smokers who succeeded in their smoking cessation attempts and those who did not. RESULTS: Among smokers, 60.8% made smoking cessation attempts and 17.9% succeeded. Being younger, married, having health insurance, BMI (Body Mass Index) > 25kg/m 2 , and having a health care provider were independent and significant predictors of both quitting attempts and quitting success. African American race and having children in the household were positively and significantly associated with quitting attempts but had an inverse relationship with quitting success. Additional significant predictors of quitting attempts were Hispanic ethnicity and the presence of smoking-related diseases. Social support was an additional predictor of quitting success. CONCLUSIONS: Our findings indicate that some predictors related to smoking cessation attempts and successful smoking cessation were different. Sub-groups of patients (e.g. ethnic minorities, patients with smoking-related disorders, patients with children) might be motivated to initiate smoking cessation but might particularly benefit from additional support such as pharmacological interventions.

Uncovering Longitudinal Health Care Behaviors for Millions of Medicaid Enrollees: A Multistate Comparison of Pediatric Asthma Utilization

“…11 Health care utilization has been the topic in many health care studies, with most studies explaining the frequency of utilization with respect to patient characteristics and other determinants of utilization for various conditions, [12][13][14][15][16][17][18][19][20][21][22] among others. Most studies rely on survey data 16,[23][24][25] and claims data, [26][27][28][29][30][31][32] with few using electronic health records. 33 Our study is novel in that it provides a framework for studying longitudinal health care utilization from patient-level data, summarizing a large set of medical visits into a series of probabilistic inferences and visual displays that are easier to understand and manage by decision makers.…”