Original ArticleRelationship between obesity, depression, and disability in middle-aged women
Introduction
There is a strong bi-directional relationship between obesity and depression in women [1], [2], [3], [4], [5]. Our prior work indicates that the prevalence of moderate and severe depression increases fourfold when comparing women with a normal body mass index (BMI 18.5–24.9 kg/m2; prevalence of depression 7%) to those with severe obesity (BMI ≥35 kg/m2; prevalence of depression 26%) [1]. Similarly, the prevalence of obesity (BMI ≥30 kg/m2) is strongly associated with depression, occurring in 25% of women with no depressive symptoms and in 58% of women with moderate and severe depressive symptoms.
There is also strong evidence that obesity and depression are each associated with disability [6], [7], [8], [9], [10], [11], [12], and the World Health Organization currently ranks depression as the leading cause of disability worldwide [13]. However, few studies have assessed the inter-relationships between obesity, depression, and disability. One study found a relationship between obesity and disability in adults with diabetes, but they found no relationship between depression and disability in the same cohort [14]. Conversely, another study of preretirement adults found that depression was strongly associated with developing activities of daily living disability after adjustment for obesity; however, this study did not report on the inter-relationships between depression, obesity, and disability [15].
The present paper uses data from a large sample of middle-aged women enrolled in a prepaid health plan to assess [1] whether obesity and depression have independent effects on disability [2], whether obesity and depression have specific effects on different domains of disability [3], whether the combination of obesity and depression exacerbates disability (i.e., multiplicative effects), and [4] the overall population impact of obesity and depression on disability and absenteeism.
Section snippets
Materials/subjects and methods
We conducted this study at Group Health, a mixed-model, health insurance and care delivery system serving approximately 600,000 members in Washington State. Study procedures were approved by the Group Health Institutional Review Board. Group Health enrollment is predominately White (85%), with higher education levels than the regional community. Compared to the rest of the United States, Group Health members are more likely to be Asian or Pacific Islanders (9% versus 4%), but less likely to be
Survey response
Of the 8000 potential participants who were mailed invitation letters, 442 were found to be ineligible (had since died, moved away, or disenrolled from the health plan). Of the remaining 7558 eligible women, 865 could not be reached by telephone and 2033 declined to participate, leaving 4660 participants (62% of those eligible). Nineteen participants had missing data on one or more key variables, so 4641 were included in analyses presented here. Participation varied significantly across
Discussion
Obesity and depression are both very common health conditions among middle age women. In this large, representative cohort from a single health plan in Washington State, we found that obesity was a significant predictor of greater disability in the areas of mobility, role functioning, household functioning, and work, but not the cognition, self-care, and social domains. Notably, women who were overweight, but not obese, were no more likely to report disability than normal weight women after
Conflict of interest
The authors declare no conflict of interest.
Acknowledgments
This project was supported by NIH Research Grant #MH68127 funded by the National Institute of Mental Health and the Office of Behavioral Social Sciences Research. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Dr. Arterburn had full access to the data in the study and takes responsibility for the integrity of the data and accuracy of the data analysis.
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