Food insecurity, coping strategies and glucose control in low-income patients with diabetes.
OBJECTIVE
To examine the relationship between food insecurity and coping strategies (actions taken to manage economic stress) hypothesized to worsen glucose control in patients with diabetes.


METHODS
Using a cross-sectional telephone survey and clinical data, we compared food-insecure and food-secure individuals in their use of coping strategies. Using logistic regression models, we then examined the association between poor glucose control (glycated Hb, HbA1c≥8·0 %), food insecurity and coping strategies.


METHODS
An urban medical centre, between June and December 2013.


METHODS
Four hundred and seven adults likely to be low income (receiving Medicaid or uninsured and/or residing in a zip code with >30 % of the population below the federal poverty level) with type 2 diabetes.


RESULTS
Of respondents, 40·5 % were food insecure. A significantly higher percentage of the food-insecure group reported use of most examined coping strategies, including foregone medical care, participation in the Supplemental Nutrition Assistance Program (SNAP)) and use of emergency food programmes. Food insecurity was associated with poor glucose control (OR=2·23; 95 % CI 1·22, 4·10); coping strategies that were more common among the food insecure were not associated with poor glucose control. Among the food insecure, receipt of SNAP was associated with lower risk of poor glucose control (OR=0·27; 95 % CI 0·09, 0·80).


CONCLUSIONS
While food insecurity was associated with poor glucose control, most examined coping strategies did not explain this relationship. However, receipt of SNAP among food-insecure individuals was associated with better diabetes control, suggesting that such programmes may play a role in improving health.
1Department of Population Health Science and Policy,Icahn School of Medicine at Mount Sinai,1 Gustave L. Levy Place,Box 1077,New York,NY 10029,USA.
Victoria L
VL
Mayer
3School of Arts and Sciences,University of Pennsylvania,Philadelphia,PA,USA.
Kevin
K
McDonough
4Division of General Internal Medicine,University of California San Francisco,San Francisco,CA,USA.
Hilary
H
Seligman
5Department of Biostatistics and Epidemiology,University of Pennsylvania,Philadelphia,PA,USA.
Nandita
N
Mitra
6Leonard Davis Institute of Health Economics,University of Pennsylvania,Philadelphia,PA,USA.
Judith A
JA
Long
D000223:Adaptation, Psychological; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001786:Blood Glucose / Q000378:metabolism*; D015992:Body Mass Index; D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000178:diet therapy*; D005260:Female; D063105:Food Assistance; D005523:Food Supply; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D007182:Income; D007328:Insulin / Q000008:administration & dosage / Q000097:blood; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000097:blood / Q000178:diet therapy; D012307:Risk Factors; D012959:Socioeconomic Factors; D011795:Surveys and Questionnaires
D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007328:Insulin
Diabetes; Disease management; Nutrition; Socio-economic factors; Vulnerable populations