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.

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%.

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.

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.

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.

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.

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].

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].

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.

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].

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.