Abstract:Conceptual frameworks provide guidance for the use of multivariate techniques and aid the interpretation of their results in the light of social and biological knowledge.
“…Age was added to the models because of its clinical significance, irrespective of the associated bi-variate p-value. To estimate the independent effects of factors on BMI, a Sex-specific, hierarchical linear regression analysis was [33]. Model 1 was built from sociodemographic factors including age, marital status, level of education, SES, and employment status (only in male model).…”
Background: The study was conducted in the Dikgale Health and Demographic Surveillance System (DHDSS) site where we have observed increasing obesity levels, particularly in women, despite evidence of high physical activity (PA) and a relatively low daily energy intake.
Objective: This study aimed to assess the socio-demographic, behavioural and biological determinants of body mass index (BMI) in adult residents permanently residing in the DHDSS.
Methods: A cross-sectional study was conducted in which socio-demographic, behavioural and biological characteristics from 1143 participants (aged 40–60 years) were collected using a paper questionnaire and standard anthropometric measures. Human immunodeficiency virus (HIV) testing was performed on all participants except those who indicated that they had tested positive. Chi-square and Mann-Whitney tests were used to analyze categorical and continuous variables, respectively, while hierarchical multivariate regression was used to analyze predictors of BMI.
Results: The median age of women and men was 51 (46–56) and 50 (45–55) years, respectively. The prevalence of overweight-obesity was 76% in women and 21% in men. A significant negative association of BMI with HIV and smoking and a significant positive association with socio-economic status (SES) was observed in both sexes. In women, BMI was negatively associated with sleep duration (p = 0.015) and age (p = 0.012), but positively associated with sugar-sweetened beverages (SSBs) (p = 0.08). In men, BMI was negatively associated with alcohol use (p = 0.016) and positively associated with being married (p < 0.001). PA was not associated with BMI in either sexes. Full models explained 9.2% and 20% of the variance in BMI in women and men, respectively.
Conclusion: BMI in DHDSS adults is not associated with physical inactivity but is associated wealth, marital status, sleep, smoking, alcohol use, and HIV status. Future studies should explore the contribution of nutrition, stunting, psycho-social and genetic factors to overweight and obesity in DHDSS.
“…Age was added to the models because of its clinical significance, irrespective of the associated bi-variate p-value. To estimate the independent effects of factors on BMI, a Sex-specific, hierarchical linear regression analysis was [33]. Model 1 was built from sociodemographic factors including age, marital status, level of education, SES, and employment status (only in male model).…”
Background: The study was conducted in the Dikgale Health and Demographic Surveillance System (DHDSS) site where we have observed increasing obesity levels, particularly in women, despite evidence of high physical activity (PA) and a relatively low daily energy intake.
Objective: This study aimed to assess the socio-demographic, behavioural and biological determinants of body mass index (BMI) in adult residents permanently residing in the DHDSS.
Methods: A cross-sectional study was conducted in which socio-demographic, behavioural and biological characteristics from 1143 participants (aged 40–60 years) were collected using a paper questionnaire and standard anthropometric measures. Human immunodeficiency virus (HIV) testing was performed on all participants except those who indicated that they had tested positive. Chi-square and Mann-Whitney tests were used to analyze categorical and continuous variables, respectively, while hierarchical multivariate regression was used to analyze predictors of BMI.
Results: The median age of women and men was 51 (46–56) and 50 (45–55) years, respectively. The prevalence of overweight-obesity was 76% in women and 21% in men. A significant negative association of BMI with HIV and smoking and a significant positive association with socio-economic status (SES) was observed in both sexes. In women, BMI was negatively associated with sleep duration (p = 0.015) and age (p = 0.012), but positively associated with sugar-sweetened beverages (SSBs) (p = 0.08). In men, BMI was negatively associated with alcohol use (p = 0.016) and positively associated with being married (p < 0.001). PA was not associated with BMI in either sexes. Full models explained 9.2% and 20% of the variance in BMI in women and men, respectively.
Conclusion: BMI in DHDSS adults is not associated with physical inactivity but is associated wealth, marital status, sleep, smoking, alcohol use, and HIV status. Future studies should explore the contribution of nutrition, stunting, psycho-social and genetic factors to overweight and obesity in DHDSS.
“…To explore associations with BMI, first a univariate linear regression was conducted, from which factors with p value less than 0.20 were included in a model. The selection of the factors was not based purely on statistical tests but a theoretical conceptual frame work (Figure 1) as proposed by Victoria et al [21], since more traditional level p values such as 0.05 used to select variables can fail in identifying variables known to be important [22]. Variables were grouped into socio-demographic, behavioural and biological categories.…”
Background: Body mass index (BMI) is rising globally with a faster increase in urban areas in low- and middle-income countries. It is critical to identify modifiable risk factors for BMI to prevent the occurrence of associated health consequences.
Objective: To investigate socio-demographic, behavioural and biological factors associated with BMI in Nairobi slums.
Methods: In 2014-2015, a cross sectional study of men and women aged 40–60 years in Nairobi slums (Korogocho and Viwandani) was conducted. Data were collected on socio-demographic, behavioural and biological characteristics. Mean BMI, overweight and obesity were computed. Hierarchical multiple linear regression analysis was conducted separately for men and women to explore factors associated with BMI.
Results: In total, 1942 study participants (54.4%, women) with a mean age (SD) of 48.3 (5.3) years and 48.8(5.6) years for women and men respectively were recruited. Mean BMI was higher among women than men (27.6 versus 22.8; p < 0.001). More women were overweight (30.9% versus 19.6%; p < 0.001) and obese (32.1% versus 5.1%; p < 0.001) than men. Among men, BMI was independently associated with wealth index, bread consumption and self-reported diabetes and was negatively associated with current tobacco smoking, HIV and TB infections. Among women, BMI was independently associated with wealth, current non-problematic drinking, and sedentary time, but was lower among other ethnicities compared to Kikuyu, among current smokers, women with longer sleep, and those with HIV infection and tuberculosis. Wealth index contributed the most variance in BMI among women and men (10.4%, 7.5%, respectively), but behavioural factors (7.4%) among men and biological factors (6.5%) among women accounted for most of the additional BMI variance.
Conclusions: Adults aged 40–60 years in the urban slums of Nairobi have a high BMI associated with wealth. Bread consumption by men and sedentary life among women are the main risky behaviours that need urgent targeted interventions.
“…A hierarchical regression model was used to determine how each of these factors are independently associated with BMI and how they also interact with each other to influence BMI in both men and women. This method of data analysis is often used in studies where the determinants of disease are being sought, and where many possible risk factors are present and may influence the disease directly or via mediation of other factors [34]. This complex interaction of risk factors and their logical sequence in the models is normally depicted using a conceptual framework, as shown in Figure 2.10.1080/16549716.2018.1467588-F0002Figure 2.Conceptual framework for hierarchical regression analysis for men and women.*Factors peculiar to women only.
…”
Background: Obesity and associated cardiometabolic diseases are increasing in urban sub-Saharan Africa due to a complex epidemiological and nutritional transition. Related data on rural communities is scarce.
Objectives: The study characterized the socio-demographic and behavioural factors influencing body mass index (BMI) among adults in rural Northern Ghana
Methods: A population-based cross-sectional study involving adults aged 40–60 years residing in the Kassena-Nankana districts was undertaken. Demographic, socio-economic and behavioural data were collected along with measures of anthropometry. We determined factors associated with BMI among women and men.
Results: A total of 2014 adults were studied. The median age was 51 (IQR 45–57) years and 54% were women. The prevalence of overweight/obesity was higher among women than men (18.4% vs. 7.2%; p < 0.001), whilst underweight was more prevalent in men (18.3% vs. 13.1%; p = 0.001). Participants with the highest level of education and a high household socio-economic status had higher BMIs than those in the lowest strata in both men (β = 0.074, p = 0.028 and β = 0.072, p < 0.001, respectively) and women (β = 0.174, p = 0.001 and β = 0.109, p < 0.001, respectively). Men (β = −0.050; p < 0.001) and women (β = −0.073; p < 0.001) of the Nankana ethnic group had a lower BMI than the Kassena ethnic group. Among men, alcohol consumption (β = −0.021; p = 0.001) and smoking (β = −0.216; p < 0.001) were associated with lower BMI. Smokeless tobacco was associated with lower BMI among women. Pesticide exposure was associated with higher BMI (β = 0.022; p = 0.022) among men.
Conclusion: Age, sex, ethno-linguistic group and prevailing socio-demographic and behavioural factors within this rural community in Northern Ghana influence BMI. The observed positive association between pesticide use and BMI warrants further investigation.
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