Introduction
Background
The shift in disease patterns has been connected with increased body weight burden, becoming a major public health concern in Uganda, as previous studies have assessed overweight or obesity among certain populations. However, little is known about body weight burden (underweight, overweight, and obesity) among women aged 15–49 years.
Objectives of the study
Main objective
Therefore, this study was conducted to identify the prevalence and its associated socio-demographic correlates of body weight categories among women of reproductive age in Uganda.
Specific objectives
To determine the association between the body weights and place of residence
To determine if the body weights vary with wealth quintiles
To determine the relationship between the body weights and the level of education of the respondents.
To determine the association between the body weights and the region and marital status.
Methods
Data source
This study utilised the data from the sixth’s series of the Ugandan Demographic and Health Survey, conducted in 2016 (2016 UDHS). The 2016 UDHS is a nationally representative sample survey of 20,880 households; 18,506 eligible women in the age range 15–49 years were interviewed with an average of 1,200 complete interviews per domain.
Description of the Measurement of BMI and Its Classification
During the 2016 SADHS, field workers used the portable height/length board in measuring height in centimetres, which was later converted to metres, with restrictions to 1.0–2.7 m (32). Weights were measured using Seca 213 portable stadiometers and formed the boundary of 20–350 kg as advocated by the WHO (33, 34).
BMI is calculated with the metric system as follows:
BMI = Person′s weight (kg) / Person′sheight (m)2
(where kg is kilogrammes and m is metres).
Note that height is commonly measured in centimetres (cm), and height (cm) is divided by 100 to obtain height in metres (m).
Outcome Variable
The outcome variable for this study was BMI, which was categorized as underweight, normal, overweight and obese, respectively. According to WHO, Women with BMI < 18.5 kg/m2 were described as underweight, while those with BMI of 18.5-24.9 kg/m2 were described as having normal body weight, those with BMI of 25-29.9 kg/m2 were overweight, and those with BMI ≥ 30 kg/m2 were obese.
Explanatory Variables
The selected socio-demographic predictor factors incorporated in the analysis are age (15 - 49 years), region (kampala , south buganda , north buganda , busoga , bukedi , bugisu, teso , karamoja, lango, acholi, west nile, bunyoro, tooro, ankole, kigezi), marital status (never married and ever married), education level (no education, primary, secondary and higher (tertiary and university)), Residence(urban and rural),and wealth quintile (lowest, middle and highest).
Statistical Analysis
Data Importation and cleaning
Step 1: Choosing the necessary variables for analysis
The code chunk below imports the following variables for analysis;
| Variable Name | Variable description |
|---|---|
| caseid | case identification |
| v012 | respondent’s current age |
| v438 | respondent’s height in centimeters (1 decimal) |
| v437 | respondent’s weight in kilograms (1 decimal) |
| v025 | type of place of residence |
| v024 | region |
| v106 | highest educational level |
| v190 | wealth index combined |
| v501 | current marital status |
Step 2: Assigning variable names
Assign descriptive names to the newly imported variables and then execute the code below.
Step 3: Creating new variables for analysis
Under this stage we aim at creating new variables like bmi, wealth and marital status category for easy analysis of the data.
Step 4: Reveal coded values
Transforming all variables into regular R factors.
Tabulations and Graphical representation of analysis
Prevalence of Body Mass Index
Figure 1 illustrates the three body weight categories of nutrition among women of reproductive age in Uganda. The bar chart reveals that a majority of the women aged 15–49 years were normal (69%) followed by those in the overweight/obese body weight category (22%).
Prevalence of Body Mass Index across the age of respondents.
Figure 2 shows that women of reproductive age (15 - 24 years) are more likely to be underweight or normal 50% or 48% respectively compared to being overweight/obese (27%), This implies that many women of age 15-24 years are either normal or underweight. However, majority of Women aged (25-29) years are overweight/obese (19%) of those who are overweight/obese.
Place of residence and Education level correlates with the BMI weights
Figure 3 shows that majority of the normal women from rural areas attended primary as their highest level of education compared to those in urban areas where majority of them attended secondary as their highest level of education.
Majority of the underweight women from rural and urban areas reported to attend primary level as their highest level of education.
The highest number of women in urban areas who are overweight/obese reported to attain secondary level as their highest level of education whereas those in rural areas reported to attend primary as their highest level of education.
Prevalence of BMI weights across the different regions
| region | underweight (%) | normal (%) | overweight/obese (%) |
|---|---|---|---|
| kampala | 4 | 54 | 42 |
| south buganda | 5 | 57 | 38 |
| north buganda | 5 | 64 | 32 |
| busoga | 7 | 76 | 17 |
| bukedi | 10 | 73 | 17 |
| bugisu | 9 | 73 | 18 |
| teso | 14 | 73 | 13 |
| karamoja | 31 | 65 | 4 |
| lango | 13 | 76 | 11 |
| acholi | 14 | 77 | 9 |
| west nile | 15 | 76 | 9 |
| bunyoro | 8 | 68 | 25 |
| tooro | 4 | 69 | 28 |
| ankole | 5 | 66 | 29 |
| kigezi | 2 | 67 | 31 |
Figure 4 shows that across all the regions most of the women are normal. The regions of karamoja(31%),lango(13%) and acholi(14%) registered the highest number of underweight women compared to those who were overweight/obese at 4%, 11% and 9%.