title: Nonlinearity in demographic and behavioral determinants of morbidity.
abstract: OBJECTIVE
  To examine nonlinearity of determinants of morbidity in the United States
  
  
  METHODS
  A secondary analysis of data on individuals with dietary data from the Cancer Epidemiology Supplement and National Health Interview Survey (NHIS) 1987, a cross-sectional, stratified random sample of the U.S. population (n = 22,080).
  
  
  METHODS
  A statistical exploration using additive multiple regression models.
  
  
  METHODS
  A Morbidity Index (0-30 points), derived from 1987 National Health Interview Survey data, combines number of conditions, hospitalizations, sick days, doctor visits, and degree of disability. Behavioral (health habits) variables were added to multivariate models containing demographic terms, with Morbidity Index and Self-assessed Health outcomes (n = 17,612). Tables and graphs compare models of morbidity with self-assessed health models, with and without behavioral terms. Graphs illustrate curvilinear relationships.
  
  
  RESULTS
  Morbidity and health are associated nonlinearly with age, race, education, and income, as well as alcohol, diet change, vitamin supplement use, body mass index (BMI), marital status/living arrangement, and smoking. Diet change and supplement use, education, income, race/ethnicity, and age relate differently to self-assessed health status than to morbidity. Morbidity is strongly associated with income up to about dollars 15,000 above poverty. Additional income predicts no further reduction in morbidity. Better health is strongly related to both higher income and education. After controlling for income, black race does not predict morbidity, but remains associated with lower self-assessed health.
  
  
  CONCLUSIONS
  Good health habits, as captured in these models, are associated with a 10-20-year delay in onset and progression of morbidity.
authors.affiliation: Public Health Institute, Oakland, CA 94607-4046, USA.
authors.forename: Jean C
authors.initials: JC
authors.lastname: Norris
authors.forename: Mark J
authors.initials: MJ
authors.lastname: van der Laan
authors.forename: Sylvia
authors.initials: S
authors.lastname: Lane
authors.forename: James N
authors.initials: JN
authors.lastname: Anderson
authors.forename: Gladys
authors.initials: G
authors.lastname: Block
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001294:Attitude to Health; D003710:Demography; D005260:Female; D015438:Health Behavior; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008875:Middle Aged; D009017:Morbidity / Q000639:trends*; D017711:Nonlinear Dynamics; D012647:Self-Assessment; D012959:Socioeconomic Factors; D014481:United States / Q000453:epidemiology