Abstract

Problem Considered: To determine the baseline rate of diabetes screening uptake in Burkina Faso and compare sociodemographic features, lifestyles, and anthropometric and glycaemic characteristics of adults who had ever and never been screened, using the first nationally representative community survey. Methods: Secondary-analysis of the cross-sectional study using the first Burkina Faso WHO STEPS survey. The sociodemographic factors, lifestyle awareness and practices and anthropometric and glycaemic features were compared between ever and never screened adults, using Chi-quare, Fisher’s Exact and Student tests and logistic regressions, in the sample of 4125 adult participants. Results: Only 5.6% (with a wide range from 1.0% to 15.0% across the country’s 13 regions) of participants have ever been screened and more frequently by urban people (17.0%), educated (22.7%) earn workers (34.6%), participants who had previously received at least a healthy lifestyle advice from a health professional (10.1%), overweight participants (10.9%), obese (19.5%) and those with abdominal obesity (12.7%). The prevalence of hyperglycaemia was 5.4 % (9.5% and 5.2% in ever and never screened participants, respectively, p = 0.0001). In logistic regression, the un-favourable sociodemographic factors for screening uptake were living in rural area, being young, un-educated, occupied without regular or formal income, while having received at least a healthy lifestyle advice (aOR = 2.1, p = 0.0001), adequate fruit and vegetables consumption (aOR = 1.9, p = 0.028), cleaning the teeth at least twice a-day (aOR = 1.5, p = 0.016), overweight or obese (aOR = 1.5, p = 0.016), increased BMI (aOR = 1.06, p = 0.0001) and abdominal obesity (aOR = 2.1, p = 0.0001) were favourable factors. Conclusion: The profile of sociodemographic disparities in diabetes screening matched that observed for hypertension and cervical cancer in Burkina Faso. Efficient dissemination of healthy lifestyles is useful for increasing screening attendance. People with normal or low body weight also need to be specifically encouraged to undergo screening. Community engagement combined with health insurance should help address unmet needs.

Keywords

Diabetes Screening, Disparities, Lifestyles, Overweight, Obesity, WHO STEPS, Burkina Faso

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1. Introduction

In the context of the epidemiological transition, diabetes has now become firmly established in low- and middle-income countries (LMICs) and particularly, it is having a dramatically increasing impact and is becoming increasingly prevalent in sub-Saharan Africa (SSA) [1]. This region faces challenges in tackling the disease, including a paucity of funding for non-communicable diseases and a limited availability of population-specific studies and guidelines [2]. Diabetes management is a process, its detection is the crucial stage [3]. If detected and treated early, the outcome is better and the burden is lower. The International Diabetes Federation (IDF) reports that SSA has the highest level of undiagnosed diabetes [4]. Therefore, specific, efficient policies for its diagnosis should be designed for LMICs, including SSA. Sociodemographic, socioeconomic and lifestyle factors interact with diabetes, especially with regard to its prevalence and access to screening and care [5]-[7]. In apparently healthy or asymptomatic subjects, the literature suggests that the uptake of cardiovascular disease (CVD) screening can be impacted by perceived threats [8], including the self-perception of having healthy body traits [9].

The World Health Organization (WHO) recommends the implementation in LMICs, of a national surveillance system for the risk factors for the noncommunicable diseases (stepwise approach to surveillance [STEPS]), especially those for CVD [10]. The WHO STEPS surveys use a standardized tool for data collection, including sections for sociodemographic features, healthy lifestyle awareness and practices, anthropometric and blood glucose measurements [10]. The STEPS survey should regularly be conducted and thus, the first survey can provide the initial overview focused on diabetes screening uptake, as undertaken for hypertension and cervical cancer screenings [9] [11].

This study aimed to determine the baseline rate of diabetes screening uptake in Burkina Faso and compare sociodemographic features, lifestyle awareness and practices, and anthropometric and glycaemic characteristics of adults who had and had never been screened. The first national community survey was used for this study.

2. Methods

2.1. Description of the First Burkina Faso STEPS Survey

The WHO STEPS surveys use a standardized tool for data collection which includes specific sections on behavioural risk factors (tobacco and alcohol consumption, fruits and vegetables intake, physical activity); anthropometric [body mass index (BMI) and waist circumference (WC)] and fasting blood biochemistry (including glycemia) [10].

The first WHO STEPS survey in Burkina Faso, conducted from 3 September to 24 October 2013, was nationally representative and covered all the country’s 13 administrative regions. It involved interviews on behavioural or lifestyle factors as well as anthropometric and FBG measurements [10]. Previous reports on methodology, including sample size calculation, sampling procedure and anthropometric and blood measurements are available elsewhere [9] [11], and only a brief description will be presented for this secondary data analysis study. The survey enrolled 4800 adults aged 25 - 64 years, and face-to-face interviews were conducted in a language spoken by the participant and data captured using personal digital assistants pre-loaded with eSTEPS software.

2.2. Study Area and Geographical Disparities’ Examining

Geographical disparities were also examined across the country’s 13 administrative regions. Each region has its own specific urbanisation rate, which has been shown in the previous study [11], including a supplementary figure https://ars.els-cdn.com/content/image/1-s2.0-S0398762023004261-mmc1.zip. The nationwide mean rate is 23.3% (minimum = 6.6%, maximum = 85.4%). The three most densely urbanized regions are “Centre” (which includes the political capital, Ouagadougou), “Hauts-Bassins” (which includes the economic capital, Bobo-Dioulasso) and “Cascades”, with respective rates of urbanisation of 85.4%, 37.6% and 19.3%.

2.3. Variables Extracted for the Present Analysis

Table 1 specifies details for the variables used in analyses.

The dependent variable was the uptake of diabetes screening by response to the yes/no question, have you ever been screened for diabetes in your lifetime? [10]. The independent variables were: The six sociodemographic factors (sex, age, residency, education level, marital status and occupation). An SSA systematic review included lifestyle practices, such as substance (alcohol and tobacco) consumption, fruit and vegetable consumption, physical activity and cleaning the teeth at least twice a-day. We assumed healthy practices can be derived from healthy lifestyle advice. For awareness, the yes/no question on the previously received of the seven healthy lifestyle awareness (advice) during the last 12 months ware asked: the abstention or cessation of i) tobacco, ii) alcohol, iii) reduced sugar diet, iv) adequate fruit and vegetables consumption, v) reduced fat diet, vi) more physical activity and vii) weight control. The lifestyle practices considered have been reported elsewhere [12], but exclusively for the hypertensive participants and were: i) use or not of at least one substance, tobacco/alcohol, ii) the practice or not of a physical activity, iii) the intake or not of at least five fruits and/or vegetables in the typical week and the iv) weight control defined using body mass index (BMI). The last independent variable to be considered is hyperglycaemia [13].

Table 1. Details on the dependent and independent variables extracted from the WHO STEPS baseline survey, for the present secondary analyses.

†: Multivariable analysis, Ref: reference when preforming multivariable analysis.

The controlled or healthy body weight was BMI under 25 kg/m². The BMI (in kg/m²) is derived from weight (in kg) and height (in m) using the formula weigh/height². Height was measured to the nearest 0.1 cm using a stadiometer (SECA 214) on a subject without shoes while weight was measured to the nearest 0.1 kg with a personal scale (SECA 813) on a lightly clothed subject without shoes. The WC was measured to the nearest 0.1 cm (as per WHO recommendations) with a measuring tape (SECA 203) at the midpoint between the last rib and the iliac crest, with the subjects standing upright and breathing normally.

There was abdominal obesity if waist circumference was 95/80 cm or more in men/women. The fasting blood glucose (in mmol/l) was measured and value of 6.1 mmol/l or more was hyperglycaemia. All measurement devices were provided by the WHO. Physical measurements were carried out on the same day.

2.4. Participants Included for the Analyses

Individuals with complete data on sociodemographic parameters, lifestyle awareness and practices, BMI, WC and FBG variables were included for analyses. Thus, data from 4125 participants were analysed.

2.5. Statistical Analysis

We used StataCorp Stata Statistical Software for Windows (Version 16.0, College Station, Texas, US) to analyse the data. The continuous variables were expressed as the means ± standard deviations, while the categorical variables were expressed as percentages (%) with 95% confidence intervals (CIs). The student’s t test was used to compare continuous variables, and the chi-square or the Fishers exact tests were used to compare categorical variables. In the stepwise logistic regression models, the dichotomized (yes/no) variable as had ever been screened for diabetes was the dependent variable. The sociodemographic factors (sex, age, residence, marital status, level of education and occupation) were among the independent variables. All the other independent variables, which included lifestyle awareness, lifestyle practices and the presence or absence of hyperglycaemia, are presented in Table 1.

2.6. Ethical Considerations

All methods were carried out in accordance with relevant guidelines and regulations. The protocol of the primary STEPS survey was approved by the Ethics Committee for Health Research of the Ministry of Health of Burkina Faso (deliberation No: 2012-12092; December 05, 2012). Written informed consent was systematically obtained from each participant in the STEPS survey. The database for this secondary analysis, is freely available from the Ministry of Health of Burkina Faso on request.

3. Results

Table 2 describes rates for diabetes screening uptake, in each of the country 13 geographic regions. The national diabetes screening uptake rate was 5.6% (95% CI: 4.9 - 6.4) and there was a significantly wide range in screening uptake, from 1.0 % to 15.0 % (p < 0.0001). For the “Centre” “Cascades” and “Hauts-Bassins” regions, the screening uptake rates were 15.0 %, 13.2 % and 8.4%, respectively. These three regions have a pooled frequency of 11.8 % (95 % CI: 10.0 - 13.9), which was 3.3 % (95 % CI: 2.8 - 4.1) for the ten remaining regions (p = 0.0001).

Table 2. Rate of diabetes screening uptake by the country geographic regions.

Overall, 50.9% of participants were female, with a mean age of 38.6 ± 11.1 years. Most participants were from rural areas (79.7%), illiterate (86.9%), and had occupation without regular and formal income (94.3%). Table 3 provides a statistical comparison of the sociodemographic parameters, lifestyles, anthropometric measurements and FBG values between individuals who had ever and never been screened for diabetes.

Table 3. Comparison of sociodemographic factors, lifestyle awareness and practices, anthropometric and glycemia values between the participants who had ever and never been screened for diabetes.

Difference in kg 8.3 kg (Std Err = 0.8), 3.0 kg/m²in BMI (Std Err = 0.3), 7.0 cm in waist circumference (Std Err=0.8), and 0.35 mmol/L in fasting blood glucose (Std Err = 0.11).

Compared with participants who had never been screened for diabetes, those who had ever been screened were more likely to have received advice on healthy lifestyle for each of the advice we considered. The participants who have previously received at least a healthy lifestyle advice from a health professional represented 33.8% (95% CI: 32.2-35.1), from whom 10.1% (95% CI: 8.5 - 11.8) had ever been screened, while only 3.4% (95% CI: 2.7 - 4.1) in those who had never been screened (p = 0.0001). Those who were abstinent with tobacco more frequently underwent for screening (6.2% vs 3.3% for smokers, p = 0.001), as well as those who clean the teeth at least twice a-day (9.8% vs 3.8% who did not brush at least twice a-day, p = 0.0001). But physically inactive participants were more frequently screened (13.2% vs 5.1% for physically active participants, p = 0.0001).

Compared to those who had never been screened, those who had ever been screened had an average weight increase of 8 kg (p = 0.0001), an increase in BMI of 3 kg/m² (p = 0.0001), an increase in waist circumference (WC) of 7 cm (p = 0.0001), and an increase in FBG of 0.35 mmol/L (p = 0.011). Overweight participants (10.9%) or obese (19.5%) more frequently underwent for screened, compared to those BMI under 25 kg/m² (5.7 %), p = 0.0001, as well as those with abdominal obesity (12.7% vs 3.7% in those without abdominal obesity, p = 0.0001).

Those who had ever been screened had an increase in FBG of 0.35 mmol/L (p = 0.011) and higher rate of hyperglycaemia (9.8%) than those who had never been screened (5.4%, p = 0.011).

Table 4 reports the associated factors with the uptake of diabetes screening in logistic regression analysis.

The associated factors with the screening uptake were living in rural area (aOR= 2.7, 95% CI: 1.9 - 3.7), being female (aOR= 1.7, 95% CI: 1.2 - 2.3), educated (aOR= 3.4, 95% CI: 2.3 - 5.0), older age of 34-49y (aOR= 2.0, 95% CI: 1.4 - 2.9) or 50 y or more (aOR= 2.8, 95% CI: 1.8 - 4.2), profession providing regular and formal income (aOR= 3.5, 95% CI: 2.3 - 5.3), having received at least a healthy lifestyle advice (aOR= 2.1, 95% CI: 1.6 - 2.9), adequate fruit and vegetables consumption (aOR= 1.9, 95% CI: 1.1 - 3.5), tooth cleaning at least twice a-day (aOR= 1.5, 95% CI: 1.1 - 2.0), overweight or obesity (aOR= 1.5, 95% CI: 1.1 - 2.1), or an increased BMI (aOR= 1.06, 95% CI: 1.03 - 1.10). When the WC replaced the BMI in the model, presence abdominal obesity was significantly associated with screening uptake (aOR= 2.1, 95% CI: 1.6 - 2.9).

Table 4. Socio-demographic factors, lifestyle, anthropometric and fasting blood glucose associations with the uptake of diabetes screening in Burkina Faso (N = 4125).

^a: When BMI was used as a categorial independent variable but not as a numeric value, and without waist circumference (categorized into yes/no abdominal obesity). ^b: When BMI was used as a numeric (in kg/m²) independent variable but not as a categorial variable, and without waist circumference (categorized into yes/no abdominal obesity). ^c: When waist circumference (categorized into yes/no abdominal obesity) was used as a categorial, and without BMI (in numeric or categorial) ^d: When fasting blood sugar was introduced as a categorial variable (categorized into yes/no hyperglycaemia) but not as a numeric (mmol/L). ^e: When fasting blood sugar was introduced as a numeric variable (mmol/L), but not as a categorial variable.

4. Discussion

The fraction of the Burkinabe population ever screened for diabetes is low, given the epidemiological transition.

4.1. Geographical, Sociodemographic Disparities in Screening Uptake

The uptake rate for diabetes screening among adults in Burkina Faso was low (5.6%; 95% CI: 4.9 - 6.4), even we found a pooled rate of 11.8% in the three mostly urbanized regions. Low screening uptake is common in Africa [14] and there was a decreased pooled rate of 3.3% for the Burkina Faso 10 regions. However, the rate of 73.1% was found in Singapore [15]. Despite the high mean FBG and hyperglycaemia rate found among those who had been screened in the bivariate analysis, there was no association between hyperglycaemia and screening uptake in the multivariable analysis, regardless of sociodemographic factors. Therefore, the weight of sociodemographic components in screening seemed consistent. Most of the socio-demographic parameters were associated with diabetes screening uptake and were consistent with the common socio-demographic or socio-economic correlates of inequalities in undiagnosed diabetes in LMICs [2], and those we identified were also addressed in South Africa [16], and elsewhere in SSA [17]. The geographic disparity as well as the unfavourable sociodemographic features identified (rural and young subjects, uneducated, occupation without regular or formal income) were also reported concerning hypertension [9] and cervical cancers [11] in Burkina Faso. In SSA, low levels of education were usually associated with low economic levels, limited access to healthcare facilities and expertise [18]. Therefore, it is appropriate to draw up coordinated interventions aimed at reducing disparities in the screening of these diseases, which can be extended to other non-communicable diseases. This may require community involvement supported by health insurance scheme. Due to the low uptake of screening for hypertension (41.6%; 95% CI: 40.0 - 43.1) [9] and cervical cancer (6.2%; 95% CI: 5.3 - 7.3) in Burkina Faso [11], an integrated cost-effective response is required [19].

4.2. Healthy Lifestyle Awareness and Practice

Individuals who have ever been screened demonstrated better knowledge on healthy lifestyle. Awareness of the healthy lifestyle driven by the health professionals to the community reflects the performance of the health care system in preventing non-communicable diseases. A third of adults have received at a least healthy lifestyle advice and this rate should be raised, considering the benefit observed on screening uptake. Indeed, the multivariable analyses confirmed that having previously received at least a healthy lifestyle advice was associated with the screening uptake. Furthermore, those who ever been screened were most likely to consume at least five portions of fruit and vegetables a day and brush their teeth at least twice a day. People who used to clean the teeth seemed to be more concerned about their physical appearance, including the whiteness of their teeth. Regular tooth cleaning may indicate a higher income level [20], a higher socioeconomic position [21] or a higher level of education, including oral health literacy [22]. The physically inactive participants were more frequently screened, as the previous analysis reported a similar trend among aware hypertensive, versus un-aware hypertensive subjects in Burkina Faso [12]. This suggests that physical inactivity may co-occur with other health conditions that increase contact with the healthcare system. The urgent need for policies to increase awareness of diabetes and to expand coverage of preventive counselling was addressed [17]. Relevant strategies for disseminating and adopting healthy practices should be considered, as even those aware of the benefits of a healthy lifestyle, such as physical activity and weight-loss diets, were no more likely to adhere to them. Effective education to promote healthy lifestyles was identified as an unmet need in diabetes management [23]. This need should be addressed at a national level within the healthcare system [23] [24].

4.3. Overweight, Global Obesity and Abdominal Obesity in Ever and Never Screened

Compared to those who had never been screened, those who had had an average increase in weight of 8 kg, an increase in BMI of 3 kg/m² and an increase in waist circumference of 7 cm. More frequent screening was observed among overweight (10.9%), obese (19.5%) and abdominal obese (12.7%) participants, suggesting that increasing anthropometric measures are a motivating factor for screening uptake. The hypothesis that thin people are less likely to be screened for hypertension has been endorsed [9], in line with the present findings regarding diabetes. Although this may be commendable, it should be acknowledged that metabolic disorders in adults with low or normal BMI (about more than four-fifths of the population) are not uncommon. Despite having a normal BMI, the excessive accumulation of fat mainly visceral, can adversely affect the lipid profile, blood pressure and intensifies inflammatory, thrombotic processes and oxidative stress. That is a type of obesity defined as metabolically obese normal weight (MONW) [25]. The MOWN among the Burkinabe population, reached 16% in women in the final quartile [26] of normal BMI. It is important to raise awareness among people with a normal or low BMI, or who consider themselves to be a normal weight, of their potential vulnerability to metabolic disorders, particularly hypertension and diabetes. Despite their conditions, they should be encouraged to undergo screening. This can be achieved by providing specific, tailored recommendations that do not cause panic or stress.

4.4. Raising Community Engagement for Diabetes Screening

Recommendations of The Lancet Commission on Diabetes, include “building community capacity in diabetes requires empowering a large workforce to deliver diabetes care in an effort to reach medically underserved communities and mitigate social determinants of health [27]”. There was evidence for the community-based screening policy in sub-Saharan Africa [28]. The contribution of healthcare professionals working in care centers in Burkina Faso, should be completed with community-based health workers, to increase the rate for healthy lifestyle awareness and screening attendance. Community-based interventions to boost diabetes screening were successfully implemented in Ghana [29]. In Ethiopia, an effective policy integrating diabetes, hypertension and cervical cancer screening was supported [30].

4.5. Strength and Limitations Screened

As the studies using the STEPS methodology should be replicated, these baseline results should be used alongside the following studies, to compute reviews’ data in national and international meta-analyses. They can also potentially contribute to developing an accurate prediction equation for updated data [31], which can further serve to outline the unmet need, with intangible cost to the health coverage system. This study shows geographic areas where urgent interventions are needed and priory targets to the stakeholders involved in tackling non-communicable diseases. The limitation of this study is the age of the data, despite which its potential value for estimating current health insurance needs has just been outlined, and the baseline evidence enables us to examine changes and make predictions. As the study is cross-sectional, it is not possible to establish whether the modifiable determinants identified preceded screening uptake, or vice versa.

5. Conclusion

The profile of geographical and sociodemographic disparities in diabetes screening matched that observed for hypertension and cervical cancer in Burkina Faso. Efficient dissemination of information about healthy lifestyles is useful for increasing screening attendance. People with a normal or low body weight as well as those who subjectively consider themselves to be a normal weight, need to be specifically encouraged to undergo screening. Community engagement in diabetes screening and the implementation of health insurance should help to identify and address unmet needs, thereby reducing sociodemographic and socioeconomic disparities. In addition to diabetes, cost-effective interventions should address hypertension and other non-communicable diseases, including cervical cancer. National health coverage is experiencing in Burkina Faso, and the present purpose is crucial, given the combined epidemiological and demographic transitions.

Acknowledgements

The authors thank the Ministry of Health for providing them with the STEPS survey database.

Authors’ Contribution

Diendéré J, Coulibaly A and Lanou HB contributed to drafting the manuscript; Diendéré J, conducted statistical analysis, Garanet F initiated the first interpretation of the results; Diendéré J, Lanou HB, Zeba AN and Diallo AH reviewed the last version. All authors reviewed and approved the final manuscript.

Conflicts of Interest

The authors of this manuscript declare that they have no conflicts of interest that are directly or indirectly related to the work submitted for publication.

References