title: Lagged Associations of Metropolitan Statistical Area- and State-Level Income Inequality with Cognitive Function: The Health and Retirement Study.
abstract: OBJECTIVE
  Much variation in individual-level cognitive function in late life remains unexplained, with little exploration of area-level/contextual factors to date. Income inequality is a contextual factor that may plausibly influence cognitive function.
  
  
  METHODS
  In a nationally-representative cohort of older Americans from the Health and Retirement Study, we examined state- and metropolitan statistical area (MSA)-level income inequality as predictors of individual-level cognitive function measured by the 27-point Telephone Interview for Cognitive Status (TICS-m) scale. We modeled latency periods of 8-20 years, and controlled for state-/metropolitan statistical area (MSA)-level and individual-level factors.
  
  
  RESULTS
  Higher MSA-level income inequality predicted lower cognitive function 16-18 years later. Using a 16-year lag, living in a MSA in the highest income inequality quartile predicted a 0.9-point lower TICS-m score (β = -0.86; 95% CI = -1.41, -0.31), roughly equivalent to the magnitude associated with five years of aging. We observed no associations for state-level income inequality. The findings were robust to sensitivity analyses using propensity score methods.
  
  
  CONCLUSIONS
  Among older Americans, MSA-level income inequality appears to influence cognitive function nearly two decades later. Policies reducing income inequality levels within cities may help address the growing burden of declining cognitive function among older populations within the United States.
authors.affiliation: Department of Health Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, Massachusetts, United States of America.|Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.|Department of Social and Behavioral Sciences, EHESP School of Public Health, Sorbonne Paris Cité, Paris Descartes University, Paris, France.
authors.forename: Daniel
authors.initials: D
authors.lastname: Kim
authors.affiliation: RAND Corporation, Santa Monica, California, United States of America.
authors.forename: Beth Ann
authors.initials: BA
authors.lastname: Griffin
authors.affiliation: University of Michigan Health System, Ann Arbor, Michigan, United States of America.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Kabeto
authors.affiliation: RAND Corporation, Santa Monica, California, United States of America.|UCLA David Geffen School of Medicine, Los Angeles, California, United States of America.
authors.forename: José
authors.initials: J
authors.lastname: Escarce
authors.affiliation: University of Michigan Health System, Ann Arbor, Michigan, United States of America.
authors.forename: Kenneth M
authors.initials: KM
authors.lastname: Langa
authors.affiliation: RAND Corporation, Santa Monica, California, United States of America.
authors.forename: Regina A
authors.initials: RA
authors.lastname: Shih
mesh_terms: D002947:Cities; D003071:Cognition / Q000502:physiology*; D005260:Female; D006262:Health / Q000191:economics* / Q000706:statistics & numerical data; D006801:Humans; D007182:Income / Q000706:statistics & numerical data*; D007407:Interviews as Topic; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D012111:Residence Characteristics; D012179:Retirement / Q000191:economics* / Q000706:statistics & numerical data; D012959:Socioeconomic Factors; D013223:Statistics as Topic; D013689:Telephone; D014481:United States