title: Predictors of food variety and dietary diversity among older persons in Botswana.
abstract: OBJECTIVE
  We investigated whether food variety and diversity are associated with physical and cognitive functioning in older adults in Botswana and designed a simple set of screening questions that predict food variety in this population.
  
  
  METHODS
  Data were collected (1998) as a national household survey of 1085 subjects 60 y and older. A food variety score, based on a food frequency checklist, was calculated by summing the frequency of weekly intakes of 16 food items (0 to 66). A dietary diversity score was calculated as the number of food groups consumed weekly (0 to 5). A representative subsample (n = 393) was randomly selected for the clinical component of the survey, and measurements on dependency and cognitive function were conducted.
  
  
  RESULTS
  Low food variety was found: 35.2%, 59.3%, and 22.4% of subjects consumed no dairy products, fruits, and vegetables, respectively. A higher food variety score was associated with urban residence, ownership of cattle, higher education, and more frequent meals, and these indicators were used to construct a nutritional risk indicator. Higher food variety score was associated with better self-reported health and better cognitive function. Similarly, a higher score on the nutritional risk indicator screening tool was associated with desirable health outcomes.
  
  
  CONCLUSIONS
  A limited number of foods is consumed, leading to an overall pattern of poor food variety. Higher food variety was associated with improved physical and cognitive functions. A screening tool that predicts food variety in this population has been developed and is recommended to be incorporated at a primary care level to identify older adults most at risk of a poor quality diet.
authors.affiliation: Department of General Practice and Community Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. thomas.clausen@medisin.uio.no
authors.forename: Thomas
authors.initials: T
authors.lastname: Clausen
authors.forename: Karen E
authors.initials: KE
authors.lastname: Charlton
authors.forename: Kesitegile S M
authors.initials: KS
authors.lastname: Gobotswang
authors.forename: Gerd
authors.initials: G
authors.lastname: Holmboe-Ottesen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D001902:Botswana; D016000:Cluster Analysis; D003071:Cognition / Q000502:physiology; D003611:Dairy Products; D004032:Diet / Q000592:standards / Q000706:statistics & numerical data; D005247:Feeding Behavior / Q000502:physiology* / Q000523:psychology*; D005260:Female; D005638:Fruit; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D015596:Nutrition Assessment; D009749:Nutrition Surveys; D011237:Predictive Value of Tests; D012959:Socioeconomic Factors; D014675:Vegetables