Sampling and spatial variation of food environments in low- and middle-income countries: Kenya

Music City Center 

Shifts in food environments (FE) are contributing to increasing risk of noncommunicable diseases (NCDs) globally. FE in low- and middle-income countries (LMIC) differ from those in high-income countries due to a preponderance of informal food vendors, rapid urbanization, and constantly changing policies. Food vendors feed their communities but remain an understudied component of FE. The informality of food vending complicates data collection, as sampling frames are unavailable, often resulting in non-representative samples. We investigate spatial variation in LMIC FE metrics and its implications for NCD risk. Using censuses of food vendors from two counties in Kenya, we simulate different sampling methods and assess representativeness. We compare sampled data to census data using various statistical distance metrics. Ignoring spatial attributes in data can introduce bias, and effective sampling designs must account for spatial autocorrelation. We measure spatial autocorrelation in samples from different sampling methods and compare them to census data. These results can guide research in LMIC by improving sample representativeness.

data representativeness

sampling bias

sampling methods

simulation

spatial autocorrelation

statistical distance 

Section on Statistics in Epidemiology