Constructing socio-economic status indices: how to use principal components analysis

Vyas, Seema; Kumaranayake, Lilani

doi:10.1093/heapol/czl029

Cited by 2,634 publications

(2,214 citation statements)

References 15 publications

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“…Employment status was categorized as either unemployed or employed (either formal or informal employment or self-employed). SES was calculated as according to the INDEPTH Demographic and Health Surveys program that involves a principal component analysis of the household assets, predicting factor scores and then categorizing these scores into quintiles [27]. Housing density was considered as the number of people in a household divided by number of rooms.…”

Section: Methodsmentioning

confidence: 99%

Determinants of body mass index by gender in the Dikgale Health and Demographic Surveillance System site, South Africa

Mashinya

Alberts

Cook

et al. 2018

Global Health Action

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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.

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Section: Methodsmentioning

confidence: 99%

Determinants of body mass index by gender in the Dikgale Health and Demographic Surveillance System site, South Africa

Mashinya

Alberts

Cook

et al. 2018

Global Health Action

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“…Wealth index was calculated using the principal component analysis method commonly used in Demographic and Health Surveys (DHS) Program [17]. …”

Section: Methodsmentioning

confidence: 99%

Sociodemographic and behavioural factors associated with body mass index among men and women in Nairobi slums: AWI-Gen Project

Asiki

Mohamed

Wambui

et al. 2018

Global Health Action

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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.

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“…Principal Component Analysis (PCA) was used to determine household wealth index from information collected on housing quality (floor, walls, and roof material), source of drinking water, type of toilet facility, the presence of electricity, type of cooking fuel, and ownership of modern household durable goods (e.g. bicycle, television, radio, motorcycle, sewing machine, telephone, cars, refrigerator, mattress, bed, computer and mobile phone) [30–33]. …”

Section: Methodsmentioning

confidence: 99%

Relationship between agricultural biodiversity and dietary diversity of children aged 6-36 months in rural areas of Northern Ghana

Saaka

Osman

Hoeschle‐Zeledon

2017

Food & Nutrition Research

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In this study, we investigated the relationship between agricultural biodiversity and dietary diversity of children and whether factors such as economic access may affect this relationship.This paper is based on data collected in a baseline cross-sectional survey in November 2013.The study population comprising 1200 mother-child pairs was selected using a two-stage cluster sampling. Dietary diversity was defined as the number of food groups consumed 24 h prior to the assessment. The number of crop and livestock species produced on a farm was used as the measure of production diversity. Hierarchical regression analysis was used to identify predictors and test for interactions.Whereas the average production diversity score was 4.7 ± 1.6, only 42.4% of households consumed at least four food groups out of seven over the preceding 24-h recall period. Agricultural biodiversity (i.e. variety of animals kept and food groups produced) associated positively with dietary diversity of children aged 6–36 months but the relationship was moderated by household socioeconomic status. The interaction term was also statistically significant [β = −0.08 (95% CI: −0.05, −0.01, p = 0.001)].Spearman correlation (rho) analysis showed that agricultural biodiversity was positively associated with individual dietary diversity of the child more among children of low socioeconomic status in rural households compared to children of high socioeconomic status (r = 0.93, p < 0.001 versus r = 0.08, p = 0.007). Socioeconomic status of the household also partially mediated the link between agricultural biodiversity and dietary diversity of a child’s diet.The effect of increased agricultural biodiversity on dietary diversity was significantly higher in households of lower socioeconomic status. Therefore, improvement of agricultural biodiversity could be one of the best approaches for ensuring diverse diets especially for households of lower socioeconomic status in rural areas of Northern Ghana.

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Constructing socio-economic status indices: how to use principal components analysis

Cited by 2,634 publications

References 15 publications

Determinants of body mass index by gender in the Dikgale Health and Demographic Surveillance System site, South Africa

Determinants of body mass index by gender in the Dikgale Health and Demographic Surveillance System site, South Africa

Sociodemographic and behavioural factors associated with body mass index among men and women in Nairobi slums: AWI-Gen Project

Relationship between agricultural biodiversity and dietary diversity of children aged 6-36 months in rural areas of Northern Ghana

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