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N.A. Al-Baghli,¹ K.A. Al-Turki,¹ A.J. Al-Ghamdi,¹ A.G. El-Zubaier,² M.M. Al-Ameer ³ and F.A. Al-Baghli ³

تضبيط السكري في الولايات الشرقية من المملكة العربية السعودية: نتائج حملة التحري

نضيرة عباس البغلي، خالد عبد الرحمن التركي، عقيل جمعان الغامدي، أحمد قاسم الزبير، محمود محمد الأمير، فاضل عباس البغلي

الخلاصـة: لتقيـيم وضع تضبيط السكري في الولايات الشرقية من المملكة العربية السعودية، دعا الباحثون جميع السكان السعوديـين ممن تزيد أعمارهم عن 30 عاماً للمساهمة في حملة تحري شملت 681 197 شخصاً اتضح أن %15.7 منهم كانوا مشخصين على أنهم سكريـين. وقد جمع الباحثون المعطيات الاجتماعية والديموغرافية والسريرية من المرضى. واتضح أن %33.8 من المرضى قد وصلوا إلى مستوى الغلوكوز المستهدف (أقل من 130 ميلي غرام/ديسي لتر في دم الأوعية الشعرية على الصيام وأقل من 180 ملي غرام عشوائياً). وأوضح التحوف اللوجستي المتعدد أن تقدُّم العمر والتدخين الحالي وانخفاض مستوى النشاط البدني يتـرافقون إلى مستوى يُعتد به إحصائياً مع عدم ضبط السكري. ويتـرافق ارتفاع ضغط الدم تـرافقاً إيجابياً مع ضبط السكر. إن المعدل الإجمالي لضبط سكر الدم منخفض لدى عامة السكان في هذه الولايات.

ABSTRACT To assess the status of diabetes mellitus (DM) control in the Eastern province of Saudi Arabia, all Saudi Arabian residents aged 30 years and above were invited to participate in a screening campaign. Of 197 681 participants screened 15.7% had a previous diagnosis of DM. Sociodemographic and clinical data were collected from these patients. Only 33.8% of patients were achieving their glycaemic control target (fasting or random capillary blood glucose < 130 mg/dL or < 180 mg/dL respectively). Multiple logistic regression analysis showed that higher age, current smoking and lower level of physical activity were significantly associated with uncontrolled DM. Hypertension was positively associated with glycaemic control. The overall rate of diabetes control is unacceptably low in the general population of this province.

Contrôle du diabète sucré dans la province orientale d’Arabie saoudite : résultats de la campagne de dépistage

RÉSUMÉEn vue d’évaluer l’état de la lutte contre le diabète sucré dans la province orientale de l’Arabie saoudite, tous les habitants âgés de 30 ans et plus ont été invités à participer à une campagne de dépistage. Sur les 197 681 personnes dépistées, 15,7 % présentaient un diagnostic antérieur de diabète sucré. Les données sociodémographiques et cliniques de ces patients ont été recueillies. Seuls 33,8 % d’entre eux atteignaient leur objectif de contrôle glycémique (glycémie à jeun ou glycémie aléatoire dans le sang capillaire 

¹Directorate of Health Affairs, Ministry of Health, Dammam, Saudi Arabia (Correspondence to N.A. Al-Baghli: This e-mail address is being protected from spambots. You need JavaScript enabled to view it ).

²College of Medicine, King Faisal University, Dammam, Saudi Arabia.

³Al-Amel Complex of Mental Health, Riyadh, Saudi Arabia.

Received: 04/06/08; accepted: 22/07/08

EMHJ, 2010, 16(6): 621-629

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Introduction

Diabetes mellitus (DM) is accompanied by long-term microvascular, neurological and macrovascular complications [1]. Glycaemic control is fundamental to the management of diabetes. The United Kingdom Prospective Diabetes Study (UKPDS) [2,3] and other randomized controlled trials [4] have demonstrated the effectiveness of good control of DM in the reduction of clinically important retinopathy, including vision-threatening lesions, and of nephropathy and neuropathy. Meta-analysis of the evidence similarly supports the potential of glycaemic control in reducing cardiovascular disease (CVD) [5]. Additional analysis indicates that therapy to achieve near normalization of blood glucose levels is cost effective compared with other treatments [6,7].

On the other hand, it has been found that, while tight glycaemic control decreases the risk of microvascular complications, it carries the risk of developing hypoglycaemia and weight gain [8]. Hence the goal of therapy is to achieve blood glucose as close to normal as possible while avoiding hypoglycaemia.

The recent recommendations of the American Diabetes Association for glycaemic control targets in adults are a glycosylated haemoglobin (HbA1c) level

In Saudi Arabia, there is a scarcity of published epidemiological data on glycaemic control in DM and the factors associated with it. The aim of this study was to assess the pattern of follow-up and status of glycaemic control in patients with a previous diagnosis of DM according to their socidemographic and clinical risk factors.

Methods

This study was part of a larger screening campaign conducted in the Eastern province of Saudi Arabia between 28 August 2004 and 18 February 2005. The methodology has been described previously [10]. A scientific committee established the detailed procedures for the campaign, including the standards for running the campaign, validation of instruments and health education materials to be used, staff training, financial supervision and data processing and entry. A media campaign was organized in each health sector (district) of the province using written and audiovisual materials, and posters on billboards in the streets and other public places.

Sample

The target population was all Saudi Arabian residents of the Eastern province of Saudi Arabia, aged 30 years and above, excluding pregnant women (650 000 subjects). They were invited to participate in a screening campaign for the early detection of DM and hypertension by attending one of the 300+ examination centres distributed in all primary health care centres, all government hospitals and most private hospitals and dispensaries, in addition to mobile teams in public venues.

The analysis described in this paper included only those participants who were previously diagnosed diabetics being managed by dietary methods or antidiabetic drugs; those who were newly diagnosed with DM during the campaign were excluded.

Data collection

A structured questionnaire for data collection was developed using information obtained from focus groups and was validated by experts in the fields of DM and hypertension. Specially trained members of health teams interviewed the participants and completed the questionnaire. Information was recorded about age, sex, place of residence, marital status, occupation, education, family income, physical activity and smoking. Current smoking was defined by subjects’ self-reports as having ever smoked > 100 cigarettes and currently smoking, every day or occasionally, for 1 month or more before the campaign any tobacco products including waterpipe (shisha). This group was compared with nonsmokers (ex- and never smokers). Physical activity at work or leisure was grouped into 4 categories: no physical activity (completely sedentary lifestyle, e.g. reading, watching TV); mild physical activity (< 3 hours per week, e.g. ordinary housework, walking), moderate exercise (3+ hours exercise per week, e.g. cycling or walking); and strenuous physical activity (5+ hours per week, e.g. jogging or swimming).

Clinical data were also obtained. Participants were asked if they had been previously diagnosed with DM and were being treated for high blood glucose and, if so, the place of treatment. The participants underwent measurements of weight, height, blood pressure and CPG. Body mass index (BMI) was calculated as weight in kilograms divided by height in metres squared. BMI 25.0–29.9 kg/m2 was classified as overweight, BMI ≥ 30.0 kg/m2 as obese, and BMI 18.5–24.9 kg/m2 as normal. Blood pressure (BP) was measured and hypertension was diagnosed based on the recommendations of the 7th report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC-VII) [11]. Whole blood glucose concentration was measured using a portable glucometer, based on reflectance photometry. Glycaemic control targets were defined as preprandial CPG  8 hours or random postprandial CPG

Coordinators were assigned for each sector to supervise the examination centres, to ensure all forms were completed, to follow up defaulters and to liaise with coordinators in other health sectors and the main supervision committees. The forms were collected from each sector and were double-checked for completeness. Ineligible people were excluded and forms with incomplete data or unconfirmed results were sent back to the health sectors with a covering letter for corrections to be made.

The participants were assured of the confidentiality of the information collected, after explaining the purpose of the campaign. In addition, health education materials were distributed to high-risk groups.

Data analysis

The differences between previously diagnosed diabetics with controlled and uncontrolled glycaemia were assessed using analysis of variance (ANOVA). The chi-squared test was used to assess the relationship between glycaemic control and socioeconomic and clinical risk factors. Cardiovascular risk factors found to be associated with uncontrolled DM were included in the multiple logistic regression and age and sex were included in the model. Age was treated as a continuous measure and the other variables as categorical measures. The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. P value

Results

Prevalence of DM and patients’ background characteristics

Data were missing for 912 people (0.5%) out of the total of 197 681 parti­cipants in the campaign. The prevalence of previously diagnosed DM was 15.7% (n = 30 798), constituting 30.4% of the target population. A higher proportion of the women were diagnosed with DM (16 307, 16.9%) than the men (14 486, 14.4%), P

Among these previously diagnosed diabetics, 97.5% were receiving treatment through different health care facilities, most commonly Ministry of Health (MOH) facilities (65.0%), followed by other government hospitals (17.9%) or private facilities (11.8%), while (2.8%) were treated in multiple health care facilities; for 1214 subjects the place of treatment was unknown.

CPG values were obtained for 30 749 (99.8%) of these patients with previously diagnosed DM. Of these, 10 384 (33.8%) were achieving the glycaemic control target. Random CPG was obtained for 22 348 patients, 37.1% of whom had controlled glycaemia (< 180 mg/dL). Fasting CPG level was obtained for 8401 patients and 24.9% had controlled fasting CPG levels (< 130 mg/dL).

Table 1 shows the mean fasting CPG and random CPG levels according to age and sex. In men the mean fasting CPG did not vary significantly with age, whereas random CPG increased with age and reached its peak in the age group 60–69 years (P

Blood pressure measurements showed that 14 423 (46.9%) patients had systolic blood pressure < 130 mmHg, 8794 (28.6%) had diastolic blood pressure

Relationship between glycaemic control and patients’ characteristics

The proportion of patients with controlled glycaemia was generally higher in the younger age groups (Table 2). More men that women had glycaemic control (P < 0.001). The highest rate of glycaemic control was found in patients managed at private health facilities and the lowest among those managed in multiple health care facilities, followed by those managed in MOH health care facilities (P < 0.001).

The proportion of patients with controlled glycaemia was generally higher in the younger age groups (Table 2). More men that women had glycaemic control The highest rate of glycaemic control was recorded among patients whose marital status was single and those with professional employment, while the lowest was noted among the widowed and self-employed (P < 0.001) (Table 3). The proportion of patients with controlled glycaemia increased as the level of education and income increased (P < 0.001).

Table 4  shows the distribution of glycaemic control in relation to geographic sectors of the Eastern province. The highest rate of glycaemic control was among patients in Khober and the lowest was in Qaria Olaya. Lower rates of glycaemic control were recorded in rural than in urban areas [588 (22.7%) versus 9795 (34.8%) (P0.001).

Risk factors for poor glycaemic control

Table 5 shows the comorbidity risk factors for patients diagnosed with DM compared with the total screened participants. The most prevalent associated risk factors for previous diagnosis of DM were positive family history of DM (19.0%), positive history of CVD (47.0%), hypertension (41.0%), obesity (19.4%) and low physical activity (18.5%), while those diagnosed with DM were less likely to be current smokers than subjects without a previous diagnosis of DM (12.1%) (P

The distribution of glycaemic control in patients with previously diagnosed DM in relation to the same risk factors is shown in Table 6. Significantly more patients who were hypertensive had controlled CPG level than those who were pre-hypertensive or non-hypertensive. Regarding BMI, the highest rate of glycaemic control was among patients who were obese, followed by those who were overweight (P

Multiple logistic regression analysis, with blood glucose control as the dependent variable, was performed to evaluate which factors were independently associated with glycaemic control in patients with diagnosed DM (Table 7). Increasing age was significantly associated with uncontrolled DM (OR = 1.02; 95% CI: 1.01–1.02, P

Moderate or strenuous physical activity, sex, BMI, being pre-hypertensive or having a history of CVD did not show any significant association with glycaemic control.

Discussion

The importance of glycaemic control in the management of DM has been highlighted by the Diabetes Control and Complications Trial [12], which found an approximately 50% to 70% reduction in the risk for retinopathy, nephropathy and neuropathy when there was intensive therapy for type 1 DM. Similar dramatic reductions in the risk of microvascular complications in type 2 DM were found in the United Kingdom Prospective Diabetes Study [3]. However, the standard of care for DM is suboptimal in most clinical settings [13–15]. In our study only one-third of diabetic patients achieved the recommended glycaemic level and less than one-quarter of them had blood pressure control. Data from the National Health and Nutrition Examination Survey in 1999–2000 showed that 35.8% of diabetics had achieved their glycaemic target, and 35.8% had achieved the target blood pressure of

The management of DM provides an excellent model for the quality of health care administered in different clinical settings and the health disparities in different regions, as illustrated by our finding that certain districts and rural populations experienced a disproportionate disease burden due to DM. This was also true for patients receiving management through MOH facilities than in other settings. Our study also provided a benchmark for quality of diabetes care across different groups, such as age, sex and socioeconomic subgroups.

The level of glycaemic control in our DM patients increased as their level of education and income increased. Populations of lower socioeconomic status have been shown to have a higher rate of diabetes-related complications and this has been attributed to a lower quality of care for these patients [15,17]. However, health care in Saudi Arabia is accessible to all and provided free of charge for the citizen population so the poor control of DM may be due to risk factors other than disparities in health care. Failure to achieve the glycaemic target in spite of the availability of efficacious treatment has been studied before, and is influenced by different factors related to the patient, provider and health care system and may be explained by a breakdown of communication related to these 3 factors [18].

Substantial attention has been focused recently on the organizational and economic aspects of medical care for diabetic patients [19] and this is reflected by our findings which suggest that better knowledge and motivation of patients plays a major part in glycaemic control and self-care practice of adults with DM. This has been highlighted by different organizations and shown to have major implications for health care policy [9,20]. A meta-analysis that reviewed the efficacy of diabetes education has found that approaches based on diet instruction and social learning were the most effective interventions for achieving glycaemic control [21,22]. Naik et al. stressed the importance of patients actively self-monitoring their blood glucose levels, and then communicating these results to their physician, who can then adjust the medication to reach the glycaemic targets [23].

In univariate analysis, obesity was associated with having glycaemic control, but regression analysis could not show a significant relationship between BMI and glycaemic control. The same was found by other researchers who attributed the anomaly to the type of cross-sectional study in which patients with good glycaemic control gain weight and patients with poor glycaemic control lose weight due to the disease process [15]. Our explanation is that this may be due to the greater concern of obese individuals to control their glycaemic level.

Good blood pressure control is a central outcome of high-quality diabetes care. The JNC VII report in 2003 recommended that blood pressure be reduced to less than 130/80 mmHg [11], due to consistent evidence that intensive control of blood pressure in adults with type 2 DM prevents both microvascular and macrovascular diseases [24,25]. Clinical trials indicated that reducing blood pressure by 10 mmHg would decrease macrovascular and microvascular complications and mortality rates by 35% [25]. Our findings revealed that individuals with DM have better control of hypertension, and this may reflect more concern and care among groups at risk than others.

The key finding of this study—that the overall rate of diabetes control in Eastern province of Saudi Arabia is unacceptably low in the general population—has important implications. Improving health care disparities in glycaemic control should be a public health priority in order to reduce diabetes-related morbidity and mortality in the community. Patients need to be empowered with the knowledge and resources to enhance their individual participation in diabetes self-care in order to improve their glycaemic control.

There were some limitations to this study. Details about management regimens and the duration of diagnosed diabetes were not known. HbA1c, which is a strong indicator of glycaemic control and which would give us a more comprehensive picture, was not measured. However, this study had its strengths, including the large sample size. Subjects with undiagnosed DM were excluded from this study, as they were not aware of their disease status and were not in a position to control their blood glucose and related cardiovascular risks. Finally, we reported the distribution of random CBG, fasting CBG level and blood pressure on the basis of clinical examination and not on records.

Acknowledgements

We thank all who participated in the campaign for their enthusiasm in fulfilling the study objectives.

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