title: Spinning the wheels and rolling the dice: life-cycle risks and benefits of bicycle commuting in the U.S.
abstract: OBJECTIVE
  To assess the net impact on U.S. longevity of the decision to commute by bicycle rather than automobile.
  
  
  METHODS
  We construct fatality rates per distance traveled using official statistics and denominators from the 2009 National Household Travel Survey. We model the life-table impact of switching from auto to bicycle commuting. Key factors are increased risks from road accidents and reduced risks from enhanced cardiovascular health.
  
  
  RESULTS
  Bicycling fatality rates in the U.S. are an order of magnitude higher than in Western Europe. Risks punish both young and old, while the health benefits guard against causes of mortality that rise rapidly with age. Although the protective effects of bicycling appear significant, it may be optimal to wait until later ages to initiate regular bicycle commuting in the current U.S. risk environment, especially if individuals discount future life years.
  
  
  CONCLUSIONS
  The lifetime health benefits of bicycle commuting appear to outweigh the risks in the U.S., but individuals who sufficiently discount or disbelieve the health benefits may delay or avoid bicycling. Bicycling in middle age avoids much fatality risk while capturing health benefits. Significant cross-state variations in bicycling mortality suggest that improvements in the built environment might spur changes in transit mode.
authors.affiliation: Economics Department, Queens College, City University of New York, Powdermaker 300, 65-30 Kissena Blvd., Queens, NY 11367, USA. Electronic address: redwards@qc.cuny.edu.
authors.forename: Ryan D
authors.initials: RD
authors.lastname: Edwards
authors.affiliation: Department of Demography, University of California, 2232 Piedmont Ave., Berkeley, CA 94720, USA. Electronic address: carlm@demog.berkeley.edu.
authors.forename: Carl N
authors.initials: CN
authors.lastname: Mason
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001642:Bicycling / Q000293:injuries / Q000502:physiology / Q000706:statistics & numerical data*; D002423:Cause of Death; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008017:Life Expectancy / Q000639:trends*; D015993:Life Tables; D008136:Longevity / Q000502:physiology; D008297:Male; D008875:Middle Aged; D018570:Risk Assessment; D014186:Transportation / Q000379:methods; D014481:United States / Q000453:epidemiology; D055815:Young Adult
keywords: Accidents; Aging; Expectations; Health; Life expectancy; Life table; Mortality