The authors have declared that no competing interests exist.
Conceived and designed the experiments: RWM DG FM ER CO SP GT. Performed the experiments: RWM. Analyzed the data: RWM DG FM ER CO SP GT. Contributed reagents/materials/analysis tools: RWM DG FM ER CO SP GT. Wrote the paper: RWM DG FM ER CO SP GT.
Few studies have examined the behavioural correlates of non-communicable, chronic disease risk in low-income countries. The objective of this study was to identify socio-behavioural characteristics associated with being overweight or being hypertensive in a low-income setting, so as to highlight possible interventions and target groups.
A population based survey was conducted in a Health and Demographic Surveillance Site (HDSS) in eastern Uganda. 1656 individuals aged 35 to 60 years had their Body Mass Index (BMI) and blood pressure (BP) assessed. Seven lifestyle factors were also assessed, using a validated questionnaire. Logistic regression was used to identify socio-behavioural factors associated with being overweight or being hypertensive.
Prevalence of overweight was found to be 18% (25.2% of women; 9.7% of men; p<0.001) while prevalence of obesity was 5.3% (8.3% of women; 2.2% of men). The prevalence of hypertension was 20.5%. Factors associated with being overweight included being female (OR 3.7; 95% CI 2.69–5.08), peri-urban residence (OR 2.5; 95% CI 1.46–3.01), higher socio-economic status (OR 4.1; 95% CI 2.40–6.98), and increasing age (OR 1.8; 95% CI 1.12–2.79). Those who met the recommended minimum physical activity level, and those with moderate dietary diversity were less likely to be overweight (OR 0.5; 95% CI 0.35–0.65 and OR 0.7; 95% CI 0.49–3.01). Factors associated with being hypertensive included peri-urban residence (OR 2.4; 95%CI 1.60–3.66), increasing age (OR 4.5; 95% CI 2.94–6.96) and being over-weight (OR 2.8; 95% CI 1.98–3.98). Overweight persons in rural areas were significantly more likely to be hypertensive than those in peri-urban areas (p = 0.013).
Being overweight in low-income settings is associated with sex, physical activity and dietary diversity and being hypertensive is associated with being overweight; these factors are modifiable. There is need for context-specific health education addressing disparities in lifestyles at community levels in rural Africa.
Non-communicable diseases (NCDs), such as cardiovascular disease (CVD), are the leading causes of adult mortality globally
Proximate risk factors for CVD, including high blood pressure and being overweight, are largely driven by behavioural factors
Most of the socio-behavioural characteristics of individuals are modifiable. It is thus important to identify these, to inform policy formulation for CVD risk reduction. The objective of this study was to identify socio-behavioural characteristics associated with being overweight or being hypertensive among people aged 35–60 years.
The study was conducted in the Iganga-Mayuge Health and Demographic Surveillance Site (HDSS)
Characteristic | -n- | % |
Sex: | ||
Males | 805 | 48.6% |
Females | 851 | 51.4% |
Location of residence: | ||
Rural | 1352 | 84.1% |
Peri-urban | 264 | 15.9% |
Age-group: | ||
35–39 | 506 | 30.6% |
40–44 | 397 | 24.0% |
45–49 | 356 | 21.5% |
50–54 | 223 | 13.5% |
55–60 | 172 | 10.4% |
Main occupation: | ||
Subsistence/Domestic | 1010 | 61.0% |
Petty trade | 169 | 10.2% |
Commercial agriculture | 143 | 8.6% |
Formal salaried | 107 | 6.5% |
Casual labour/barter | 96 | 5.8% |
Mason/Artisan | 45 | 2.7% |
Medium or large trade | 19 | 1.1% |
Not specified | 67 | 4.0% |
Highest level of education: | ||
None | 322 | 19.4% |
Lower Primary | 347 | 21.0% |
Higher Primary | 588 | 35.5% |
Secondary – O level | 287 | 17.3% |
Secondary – A level | 27 | 1.6% |
Tertiary | 85 | 5.1% |
Family history of diabetes: | ||
No | 1450 | 87.6% |
Yes | 206 | 12.4% |
SES quintiles: | ||
Lowest | 331 | 20.0% |
Second | 331 | 20.0% |
Middle | 322 | 19.4% |
Fourth | 319 | 19.3% |
Highest | 353 | 31.3% |
The study population comprised adult men and women aged 35–60 years within the HDSS. A clustered study design
Research assistants obtained physical measurements and administered a structured questionnaire. Height was measured (to two decimal places) using standard height meters, with the participant standing upright. Weight was measured using calibrated Seca® scales, with the participant lightly clothed. Waist and hip circumference were measured using a tape measure. Waist circumference was measured around a horizontal plane through the mid-point between the lower costal margin and the iliac crest, waistline unclothed. Hip circumference was measured around a horizontal plane through the trochanters. BMI was calculated as weight in kilograms divided by the square of height in metres. A participant was classified as overweight if their BMI was 25 Kgm−2 or greater. A participant was classified as being obese if their BMI was 30 or greater
Characteristic | Males | Females | Overall | p-value | ||
-n- | % | -n- | % | % | ||
BMI categories: | ||||||
Less than 18.5 | 142 | 17.6% | 124 | 14.6% | 16.1% | Ref |
18.5–24.99 | 585 | 72.7% | 513 | 60.3% | 66.3% | 0.975 |
25–29.99 | 60 | 7.5% | 144 | 16.9% | 12.3% | <0.001 |
30+ | 18 | 2.2% | 70 | 8.2% | 5.3% | <0.001 |
Waist circumference: | ||||||
Normal | 755 | 93.8% | 455 | 53.5% | 73.0% | Ref |
Moderately elevated | 39 | 4.8% | 179 | 21.0% | 13.2% | <0.001 |
Substantially elevated | 11 | 1.4% | 217 | 24.5% | 13.8% | <0.001 |
Blood pressure: | ||||||
Normal or low | 214 | 26.6% | 339 | 39.8% | 33.4% | Ref |
Pre-hypertensive | 424 | 52.7% | 339 | 39.8% | 46.1% | <0.001 |
Hypertensive or on treatment | 167 | 20.7% | 173 | 20.4% | 20.5% | 0.356 |
Both Hypertensive and Overweight: | ||||||
No | 772 | 95.9% | 783 | 92.0% | 93.9% | Ref |
Yes | 33 | 4.1% | 68 | 8.0% | 6.1% | 0.001 |
Ref = Reference category.
Two blood pressure (BP) measurements were taken (at least 5 minutes apart), with the participant seated, using a calibrated electronic BP device (Welch-Allyn®). For each participant's two BP measurements, the average was calculated to represent their BP. A participant was classified as being hypertensive if their average systolic BP was 140mmHg or higher, or if their average diastolic BP was 90mmHg or higher, or if they were on anti-hypertensive treatment
A structured questionnaire was used to collect data on socio-behavioural factors, including demographic characteristics, ownership of 11 indicator assets for purposes of assessing socio-economic status (based on the Uganda Demographic and Health Survey)
The questionnaire used to assess socio-behavioural characteristics was adapted from different tools that have been validated elsewhere. Questions on physical activity were derived from the STEPs tool, which adapts them from the WHO Global Physical Activity Questionnaire (GPAQ). The GPAQ has been shown to have a high reliability (kappa of 0.67 to 0.73 and 0.84 to 0.93), but a fairly low criterion validity (up to 0.35)
To classify socio-economic status (SES), principal components analysis (PCA) was run on the 11 household assets evaluated. These assets included owning: 1) a radio, 2) a television, 3) a mobile phone, 4) a bicycle, 5) a motorcycle, 6) a motor vehicle, 7) a piece of land, 8) large farm animals like cattle, goats and sheep, 9) small farm animals like poultry, 10) a manufactured bed, and 11) the nature of the walls of their house. The principal component on which most assets loaded was used to generate an SES score for each participant. Participants were then grouped into SES quintiles (five descending groups). The same approach is used in Demographic Surveillance Surveys in Uganda
The questions on physical activity sought information on the participants' undertaking of ‘vigorous-intensity activities’ (e.g. lifting heavy loads, digging, construction work, etc) and ‘moderate-intensity activities’ (e.g. brisk walking, carrying light loads, riding a bicycle and recreational activities like physical exercises and walking during leisure, etc). Time spent on these activities in a typical week was recorded. Participants were classified into those that met the WHO minimum recommendations for physical activity (at least 75 minutes of vigorous-intensity, or at least 150 minutes of moderate-intensity activities per week)
Psychosocial stress was measured in five domains, including anxiety, apathy, depression, fatigue and insomnia, using 5 questions adapted from Erikson et al
Knowledge about lifestyle diseases was measured in five domains using diabetes as a proxy. The domains included awareness about diabetes, and its symptoms, its risk factors, its prevention and misconceptions about diabetes. Participants were scored with a “1” for each knowledge item they knew, and with a “0” for those they did not know. The total score for the 39 items used in the assessment was used to grade participants into four knowledge levels, i.e. very-low (0–9), low (10–19), moderate (20–29) and good (30–39). In this analysis, participants in the ‘moderate’ and ‘good’ category were classified as having adequate knowledge about life-style diseases.
Tobacco use was assessed using questions on current and ever use of tobacco, and associated habits, whereas alcohol use was assessed with questions on frequency, type of alcohol and quantity consumed. Participants were classified as engaging in ‘harmful alcohol taking’ if they exceeded the recommended level for safe alcohol in-take i.e. more than 3 drinks on average every time they drink, or if they undertook binge drinking (i.e. more than 3 drinks on one occasion in the one month preceding the survey)
Food frequency was assessed using dietary recall of foods that an individual ate in the seven days preceding the survey. Local foods were grouped into 9 food groups as recommended for use in dietary diversity assessments
Data were double entered in EpiData, cleaned and exported to STATA10 for analysis. The prevalence of over-weight and hypertension were calculated as the percentage over-weight and hypertensive respectively, the total study sample being the denominator. Logistic regression analysis was used to identify the socio-behavioural factors associated with being over-weight (Refer to
The conduct of this study was approved by Makerere University School of Public Health Higher Degrees Research and Ethics Committee, the Swedish Regional Ethics Board (Stockholm) and the Uganda National Council of Science and Technology. Permission was also obtained from the Iganga-Mayuge Health and Demographic Surveillance Site (HDSS) management, and signed full informed consent sought from each participant.
Of the 1,680 eligible HDSS residents sampled, 1,656 participated in the study (98.6% response rate), of which 851 (51%) were females.
Factors | Sub-category | -n- | % Over-weight | COR[95% CI] | AOR†[95% CI] | p-value |
|
||||||
Sex: | Male | 805 | 9.7% | 1.0 | 1.0 | |
Female | 848 | 25.2% | 3.1[2.36–4.19] | 3.7[2.69–5.08] | <0.001 | |
Residence: | Rural | 1390 | 14.2% | 1.0 | 1.0 | |
Peri-urban | 263 | 35.7% | 3.3[2.48–4.52] | 2.1[1.46–3.01] | <0.001 | |
Age-group: | 35–39 | 505 | 14.5% | 1.0 | 1.0 | |
40–44 | 396 | 18.2% | 1.3[0.92–1.89] | 1.2[0.82–1.79] | 0.338 | |
45–49 | 355 | 18.9% | 1.4[0.96–1.98] | 1.6[1.07–2.40] | 0.023 | |
50–54 | 223 | 21.5% | 1.6[1.08–2.43] | 1.8[1.12–2.79] | 0.015 | |
55–60 | 172 | 18.6% | 1.4[0.86–2.14] | 1.6[0.92–2.62] | 0.093 | |
SES quintile: | Lowest | 331 | 7.7% | 1.0 | 1.0 | |
Second | 331 | 17.1% | 2.5[1.50–4.12] | 2.5[1.50–4.31] | 0.001 | |
Middle | 322 | 20.8% | 3.2[1.91–5.19] | 3.4[2.03–5.85] | <0.001 | |
Fourth | 319 | 16.9% | 2.4[1.47–4.07] | 2.4[1.36–4.08] | 0.002 | |
Highest | 353 | 24.7% | 4.0[2.42–6.44] | 4.1[2.40–6.98] | <0.001 | |
Family history of diabetes: | No | 1447 | 16.5% | 1.0 | 1.0 | |
Yes | 206 | 26.2% | 1.8[1.28–2.54] | 1.5[1.02–2.22] | 0.040 | |
Hypertensive: | No | 1314 | 14.5% | 1.0 | 1.0 | |
Yes | 339 | 29.8% | 2.5[1.88–3.31] | 2.5[1.79–3.38] | <0.001 | |
|
||||||
Attains WHO minimum physical activity level: | No | 261 | 28.0% | 1.0 | 1.0 | |
Yes | 1392 | 15.7% | 0.5[0.35–0.65] | 0.7[0.47–0.99] | 0.048 | |
Stress level: | Low | 715 | 16.8% | 1.0 | ||
Moderate | 657 | 19.0% | 1.2[0.88–1.53] | |||
High | 263 | 16.7% | 1.0[0.68–1.45] | |||
Knowledge about lifestyle diseases: | Very Low | 371 | 11.3% | 1.0 | 1.0 | |
Low | 715 | 19.4% | 1.9[1.30–2.74] | 1.7[1.13–2.55] | 0.010 | |
Moderate | 468 | 18.4% | 1.8[1.19–2.62] | 1.5[0.95–2.31] | 0.086 | |
Good | 99 | 25.3% | 2.6[1.52–4.61] | 2.0[1.03–3.70] | 0.040 | |
Tobacco user: | No | 1554 | 18.5% | 1.0 | 1.0 | |
Yes | 99 | 4.0% | 0.2[0.07–0.51] | 0.4[0.13–1.11] | 0.077 | |
Harmful alcohol taker: | No | 1570 | 18.0% | 1.0 | 1.0 | |
Yes | 83 | 10.8% | 0.6[0.27–1.12] | 0.7[0.33–1.56] | 0.399 | |
Dietary diversity: | Low | 350 | 21.1% | 1.0 | 1.0 | |
Moderate | 1138 | 16.7% | 0.7[0.55–1.01] | 0.7[0.49–0.97] | 0.033 | |
High | 165 | 17.0% | 0.8[0.47–1.23] | 0.8[0.46–1.34] | 0.369 |
Hosmer-Lemeshow Goodness-of-Fit p-value = 0.128; COR = Crude Odds Ratio; AOR = Adjusted Odds Ratio;
Modifiable factors found to be associated with being overweight included location of residence, socio-economic status, blood pressure, level of physical activity, knowledge about diabetes, and dietary diversity. Peri-urban residents were 2 times more likely to be overweight compared to rural residents (OR 2.1; 95% CI 1.46–3.01). The likelihood of being overweight increased significantly with socio-economic status. Participants in the highest SES quintile were 4 times more likely to be overweight than those in the lowest quintile (OR 4.1; 95% CI 2.40–6.98). Of the 1656 participants, 1392 (84%) met the WHO minimum recommendations for physical activity, with no significant difference between sexes (OR 1.2; 95% CI 0.92–1.57) <this finding is not included in the tables>. Participants who met the WHO minimum standard for physical activity were significantly less likely to be overweight than those who did not meet the minimum recommendation (OR 0.7; 95% CI 0.47–0.99). Participants with moderate and higher dietary diversity were also less likely to be overweight than those with low dietary diversity. As a paradoxical finding, people who knew more about diabetes were more likely to be overweight compared to the less knowledgeable.
Factors not significantly associated with being over-weight included occupation, level of education <Finding not included in the tables>, stress level, tobacco use and harmful alcohol intake.
Factors | Sub-category | -n- | % Hypertensive | COR[95% CI] | AOR‡[95% CI] | p-value |
|
||||||
Sex: | Male | 805 | 20.7% | 1.0 | ||
Female | 848 | 20.3% | 1.0[0.77–1.24] | |||
Residence: | Rural | 1390 | 19.0% | 1.0 | 1.0 | |
Peri-urban | 263 | 28.5% | 1.7[1.25–2.28] | 2.4[1.60–3.66] | <0.001 | |
Age-group: | 35–39 | 505 | 11.3% | 1.0 | 1.0 | |
40–44 | 396 | 16.9% | 1.6[1.09–2.34] | 1.4[0.97–2.12] | 0.068 | |
45–49 | 355 | 25.3% | 2.7[1.83–3.86] | 2.5[1.73–3.69] | <0.001 | |
50–54 | 223 | 28.7% | 3.2[2.10–4.78] | 3.0[1.97–4.49] | <0.001 | |
55–60 | 172 | 36.0% | 4.4[2.88–6.85] | 4.5[2.94–6.96] | <0.001 | |
SES quintile: | Lowest | 331 | 19.0% | 1.0 | ||
Second | 331 | 19.6% | 1.0[0.70–1.53] | |||
Middle | 322 | 22.0% | 1.2[0.82–1.78] | |||
Fourth | 319 | 22.5% | 1.2[0.85–1.82] | |||
Highest | 353 | 18.6% | 0.9[0.66–1.44] | |||
Family history of diabetes: | No | 1450 | 20.1% | 1.0 | ||
Yes | 206 | 23.8% | 1.2[0.88–1.76] | |||
BMI: | <25 | 1361 | 17.5% | 1.0 | ||
≥25 | 292 | 34.6% | 2.5[1.89–3.30] | 2.8[1.98–3.98] | <0.001 | |
|
||||||
Attains WHO minimum physical activity level: | No | 261 | 22.6% | 1.0 | 1.0 | |
Yes | 1395 | 20.1% | 0.9[0.63–1.19] | 1.2[0.81–1.67] | 0.401 | |
Stress level: | Low | 717 | 20.1% | 1.0 | ||
Moderate | 676 | 19.5% | 1.0[0.74–1.26] | |||
High | 263 | 24.3% | 1.3[0.91–1.79] | |||
Knowledge about lifestyle diseases: | Very Low | 371 | 16.1% | 1.0 | 1.0 | |
Low | 715 | 19.4% | 1.3[0.90–1.75] | 1.1[0.81–1.62] | 0.453 | |
Moderate | 468 | 22.0% | 1.5[1.03–2.08] | 1.3[0.88–1.85] | 0.204 | |
Good | 99 | 30.3% | 3.3[1.96–5.36] | 2.7[1.63–4.63] | <0.001 | |
Tobacco user: | No | 1554 | 20.4% | 1.0 | 1.0 | |
Yes | 99 | 22.2% | 1.1[0.68–1.82] | 1.3[0.80–2.28] | 0.267 | |
Harmful alcohol taker: | No | 1570 | 20.0% | 1.0 | 1.0 | |
Yes | 83 | 24.1% | 1.3[0.92–1.77] | 0.9[0.52–1.64] | 0.779 | |
Dietary diversity: | Low | 350 | 18.6% | 1.0 | 1.0 | |
Moderate | 1141 | 21.9% | 1.2[0.91–1.67] | 1.4[0.98–1.89] | 0.058 | |
High | 165 | 15.2% | 0.8[0.47–1.30] | 0.8[0.50–1.44] | 0.534 |
Goodness-of-Fit p-value = 0.863;
The modifiable factors found to be associated with being hypertensive included location of residence and being overweight (
As with BMI, participants who knew more about lifestyle diseases were more likely to be hypertensive, compared to the less knowledgeable (
Stratified analysis shows that not only was there an association between being hypertensive and being overweight (OR 2.5, 95% CI 1.89–3.30), but the association was significantly higher among rural residents (OR 2.9, p<0.001) than peri-urban residents (OR 1.3, p = 0.363) (p-value for homogeneity of OR = 0.013). This finding therefore shows that the relationship between being hypertensive and being over-weight is modified by interaction between BMI status and place of residence. Participants who are over-weight are more likely to be hypertensive, but this was observed only in the rural residents and not in the peri-urban areas.
This study not only assesses the prevalence of overweight and hypertension but also describes the modifiable socio-behavioural factors associated with these in a low-income setting. The finding that 18% of participants were overweight shows a high burden of overweight among people aged 35–60 years. Compared with findings from a rural cohort in southern Uganda where 11% of the general population were overweight
The non-modifiable factors found to be associated with being overweight included sex, age and family history of diabetes. Sex was the most significant of these factors, implying a possible connection with gender related factors. This is consistent with findings from other settings in sub-Saharan Africa (SSA)
This study affirms that age is an important non-modifiable factor associated with hypertension, similar to what was found in other contexts in sub-Saharan Africa
The modifiable risk factors associated with being overweight included location of residence, socio-economic status, physical activity and dietary diversity. The observed increase in likelihood of being overweight with socio-economic status and the rural-urban divide may be attributed to ability to afford the more expensive energy-dense foods
Our study finds that those more knowledgeable about lifestyle diseases were more likely to be overweight and hypertensive. This paradox may be due to hypertensive people knowing more about lifestyle diseases than non-hypertensives. However, it may suggest that knowledge alone may not be sufficient to change behaviour. The finding that harmful alcohol taking and tobacco use were not associated with being overweight or hypertensive could be due to the low volumes used in this population, or that the sample sizes for these sub-populations were not sufficient.
We recognize a number of methodological limitations in this study, including using knowledge about diabetes as a proxy for knowledge about lifestyle diseases, categorisation of variables that were initially measured as numerical variables, and use of BMI to test for associations, despite its known shortfalls. However, BMI categorizations have been calibrated and recommended by the WHO as a measure of CVD risk, and are widely used in NCD risk assessments, including the STEPs. All categorisations (blood pressure, physical activity and BMI) were based on the standard criteria recommended by the WHO. Limitations arising from using self-reports to assess lifestyles are also noted, but were mitigated by using validated tools. In addition, dietary assessment was limited to dietary diversity scores that do not take into account the quantity of nutrients eaten. A full assessment of nutritional factors was outside the scope of this study. The study was conducted in a HDSS setting, where populations know that they are under observation. However, there are no on-going interventions on NCDs.
This predominantly rural population in a low income setting has a high prevalence of overweight and hypertension. Being overweight in this setting is associated with insufficient physical activity and low dietary diversity while being hypertensive is associated with being overweight; these factors are modifiable. The policy implication of these findings is that primary health care programmes should integrate education on these lifestyle risk factors. Messages should emphasize culturally relevant ways of increasing physical activity and balanced diets. Because women are more likely to be over-weight, interventions should incorporate a gender dimension. The increased likelihood of hypertension in age groups above 45 years and in overweight persons justify the need for routine screening of people older than 45 years for hypertension, especially if they are overweight.
About one in six people between 35 and 60 years of age in this low-income setting are overweight; being overweight in low-income settings is associated with gender factors, insufficient physical activity, low dietary diversity, peri-urban residence and socio-economic status, which are modifiable risk factors.
About one in five people aged 35 and 60 years in this low-income setting is likely hypertensive; hypertension is associated with being overweight, especially among rural dwellers, and being overweight is modifiable too.
Targeted education that specifies lifestyle measures regarding diet, physical activity and overweight should be instituted at community level in low income countries.
This work was supported by the Swedish International Development Agency. The Management of Iganga-Mayuge HDSS, and the Diabetes Research Group at Karolinska Institutet, especially Agneta Hilding and Anna-Karin Eriksson, are duly acknowledged for their technical input, as is Pauline Binder, of the Women and Child Health Department, Uppsala University, Sweden, for the language audit. The Family Health and Wealth Project provided the anthropometry equipment.