title: Robustness in health research: do differences in health measures, techniques, and time frame matter?
abstract: Survey-based health research is in a boom phase following an increased amount of health spending in OECD countries and the interest in ageing. A general characteristic of survey-based health research is its diversity. Different studies are based on different health questions in different datasets; they use different statistical techniques; they differ in whether they approach health from an ordinal or cardinal perspective; and they differ in whether they measure short-term or long-term effects. The question in this paper is simple: do these differences matter for the findings? We investigate the effects of life-style choices (drinking, smoking, exercise) and income on six measures of health in the US Health and Retirement Study (HRS) between 1992 and 2002: (1) self-assessed general health status, (2) problems with undertaking daily tasks and chores, (3) mental health indicators, (4) BMI, (5) the presence of serious long-term health conditions, and (6) mortality. We compare ordinal models with cardinal models; we compare models with fixed effects to models without fixed-effects; and we compare short-term effects to long-term effects. We find considerable variation in the impact of different determinants on our chosen health outcome measures; we find that it matters whether ordinality or cardinality is assumed; we find substantial differences between estimates that account for fixed effects versus those that do not; and we find that short-run and long-run effects differ greatly. All this implies that health is an even more complicated notion than hitherto thought, defying generalizations from one measure to the others or one methodology to another.
authors.affiliation: School of Economics, Queensland University of Technology, Brisbane, Australia.
authors.forename: Paul
authors.initials: P
authors.lastname: Frijters
authors.forename: Aydogan
authors.initials: A
authors.lastname: Ulker
mesh_terms: D000368:Aged; D000465:Algorithms; D001315:Australia / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D005260:Female; D015438:Health Behavior; D006302:Health Services Research / Q000706:statistics & numerical data; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008875:Middle Aged; D009017:Morbidity; D009026:Mortality; D012923:Social Class