The relationship between obesity and elderly suicide rates: a cross-national study

Ajit Shah

doi:10.5249/jivr.v2i2.63

Abstract

:Background: An inverse relationship between obesity and suicide has been observed in younger adults, but this has not been examined in the elderly. Methods: A cross-national ecological study examined the independent relationship between the prevalence of obesity and elderly suicide rates, by controlling for potentially confounding variables, using data from the World Health Organization and the United Nations. Results: Elderly suicide rates in females were independently associated with the prevalence of obesity. Conclusions: Caution should be exercised in attributing a causal relationship from this cross-sectional ecological study due to ecological fallacy and requires confirmation in individual-level case-control or cohort studies.

Highlights

A n inverse relationship between body mass index (BMI) and suicide has been observed in male Swedish conscripts,[1] male American healthcare professionals,[2] andAmericans[3] and Norwegians 4 in the general population
A cross-national ecological study with the null hypothesis that there will be no relationship between the prevalence of obesity and national elderly suicide rates was conducted
Full data set of the national prevalence rate of obesity and suicide rates were available in males and females for 35 and 40 countries respectively

Summary

Introduction

Americans[3] and Norwegians 4 in the general population This relationship was sustained after controlling for several confounding variables including smoking, alcohol intake, education, employment, marital status, social support, physical activity, medical illness, depression, antidepressant use.[2,4]. These studies, focusing on younger age groups, suggested an inverse relationship between obesity and suicide in both sexes. Methods: A cross-national ecological study examined the independent relationship between the prevalence of obesity and elderly suicide rates, by controlling for potentially confounding variables, using data from the World Health Organization and the United Nations.

Methods

Results

Conclusion