BMI analysis

BMI analysis

Body Mass Index (BMI) is a person’s weight in kilograms (or pounds) divided by the square of height in meters (or feet). A high BMI can indicate high body fatness. BMI screens for weight categories that may lead to health problems, but it does not diagnose the body fatness or health of an individual.The evidence is inconclusive regarding an association between BMI and mental distress. Different studies have focused on different dimensions of mental health, used different definitions of mental illness, and studied different populations and many of them have not controlled for all the potential confounding factors. The primary aim of this study was to explore the association between FMD and BMI in a representative sample of U.S. adults included in the Behavioral Risk Factor Surveillance System (BRFSS) dataset of 2011. The secondary aim was to examine whether this association differed between males and females. The hypothesis was that people who do not have a normal BMI are more likely to suffer from FMD than those in the normal BMI category, even after adjusting for all the covariates.


Study Population

This study is based on the analysis of 2011 BRFSS results. BRFSS, the principal health-related telephone survey among a representative sample of U.S. residents aged 18 years and above, collects information about the respondents’ risk behaviors and events affecting health, chronic health conditions, and use of preventive services. A total of more than 506,000 interviews were conducted in 2011

2. Measuring FMD

The BRFSS questionnaire has an item asking the respondent to report the number of days his/her mental health was not good in the previous 30 days [43]. All the participants who had 14 or more days of “not good” mental health in the previous month were categorized as having FMD [4] and the rest were categorized as not having FMD.

3. Independent Variables

The variable categorizing individuals based on their BMI was the main predictor for this analysis. Age categories in years, gender, race/ethnicity, level of education completed, employment status, income level, and marital status were the sociodemographic covariates. Tobacco and alcohol use were considered. Other lifestyle factors included were number of healthy food items and physical activity. The number of obesity related chronic conditions was taken into account, depending on whether a health professional ever told them of having high blood pressure, high cholesterol, heart disease, stroke, asthma, diabetes, or arthritis.

4. Statistical Analyses

The respondents who answered as “don’t know/not sure,” “refused,” or had missing responses at random were excluded from the analyses. Women who reported being told of having high blood pressure and/or diabetes only during their pregnancy were also excluded. Logistic regression analysis was used to investigate the association of FMD with BMI and other covariates. Each of the independent variables was separately used to predict FMD. This was followed by a multivariable model, where all the variables were simultaneously introduced. Finally, separate multivariable analyses were performed after stratifying for gender. All the independent variables were categorical, except the number of healthy food items (0–4) and number of ever-diagnosed chronic conditions (0–6), which were treated as continuous in multivariable analysis. All analyses were performed using SAS statistical software package, version 9.3 (SAS Institute Inc., Cary, North Carolina). Adjustments were made for the sampling design and for the raking procedure used to assign respondent weights  by using “proc survey” procedures in SAS 



All the variables were self-reported by the respondents and could be subject to recall bias. People may tend to underreport mental distress due to social-acceptability bias. Besides, the benchmark for having “good” or “not good” mental health can vary from person to person. Quality of sleep and its duration can affect both BMI and mental health. Unfortunately, sleep related variables could not be included in this analysis, because of very few valid responses. Another potential confounder not taken into account is the intake of certain psychiatric medications, which can lead to weight gain. The way in which some of the variables were combined for operational purposes (e.g., diet) was arbitrary and might not have been the best way to do so. For most of the questions, there were respondents who refused to answer or responded as “don’t know/not sure.” These people, excluded from analysis, could be different in their behaviors, resulting in self-selection bias. However, a comparison of the characteristics of the sample  indicates that the percentages are fairly similar. Another drawback was that, after excluding all the missing values for all the variables, the sample size decreased considerably compared to bivariate analysis. This might partially be responsible for the differences in odds ratios between bivariate and multivariable analysis results. BMI is not always the most reliable indicator of body fat, and factors like the individual’s waist circumference were not included in the survey. Mental health problems such as depression and anxiety, are not uncommon during pregnancy and it would be nice to look at the relationship of prepregnancy BMI, gestational gain in body weight, and mental distress among pregnant women . This was a cross-sectional survey; hence causality cannot be inferred. Also, there was no opportunity to evaluate the association of individual mental health disorders separately with the independent variables. Chronic health conditions have been grouped together, but some specific disorders, such as diabetes, are found to be associated with mental disorders, such as depression


This study has used data from a very recent nationally representative sample. FMD, an indicator of Health-Related Quality of Life, indicates the assessment of a person about his or her own mental well-being . A lot of confounding factors have been taken into account. The findings suggest that there could be a relationship between BMI and FMD independent of sociodemographic characteristics, risk-behaviors, lifestyle factors, and chronic diseases. Future research should explore longitudinal trend, whether abnormal BMI from an early age precedes mental distress, or vice versa. Measuring stigma and discrimination experienced by an overweight, obese, or underweight individual would be vital in understanding their role as potential mediators.