Predictors of food variety and dietary diversity among older persons in Botswana.
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.
Clausen
Thomas
T
Department of General Practice and Community Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. thomas.clausen@medisin.uio.no
Charlton
Karen E
KE
Gobotswang
Kesitegile S M
KS
Holmboe-Ottesen
Gerd
G
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