Methodological Considerations When Studying the Association between Patient-Reported Care Experiences and Mortality

Health Serv Res. 2015 Aug;50(4):1146-61. doi: 10.1111/1475-6773.12264. Epub 2014 Dec 7.

Abstract

Objective: To illustrate methodological considerations when assessing the relationship between patient care experiences and mortality.

Data source: Medical Expenditure Panel Survey data (2000-2005) linked to National Health Interview Survey and National Death Index mortality data through December 31, 2006.

Study design: We estimated Cox proportional hazards models with mortality as the dependent variable and patient experience measures as independent variables and assessed consistency of experiences over time.

Data extraction methods: We used data from respondents age 18 or older with at least one doctor's office or clinic visit during the year prior to the round 2 interview. We excluded subjects who died in the baseline year.

Principal findings: The association between overall care experiences and mortality was significant for deaths not amenable to medical care and all-cause mortality, but not for amenable deaths. More than half of respondents were in a different care experience quartile over a 1-year period. In the five individual experience questions we analyzed, only time spent with the patient was significantly associated with mortality.

Conclusions: Deaths not amenable to medical care and the time-varying and multifaceted nature of patient care experience are important issues to consider when assessing the relationship between care experience and mortality.

Keywords: Patient care experiences; mortality; quality of care.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Age Factors
  • Ambulatory Care Facilities / statistics & numerical data*
  • Female
  • Health Services Research
  • Humans
  • Male
  • Middle Aged
  • Mortality*
  • Office Visits / statistics & numerical data*
  • Patient Satisfaction / statistics & numerical data*
  • Proportional Hazards Models
  • Quality of Health Care / statistics & numerical data*
  • Sex Factors