Abstract
Background
Diabetes is becoming a worldwide concern. Optimal diabetes control reduces diabetes complications.
Objectives
We aimed to measure the principal diabetes care parameters to recognize the main deficits in care for patients with diabetes in the region.
Methods
This cross-sectional study is based on the Khuzestan Comprehensive Health Study (KCHS) data. Of all participants, 4673 (15.3%) were identified to have diabetes. We invited the known cases of diabetes to complete a checklist about their diabetes through face-to-face interviews, and we obtained a blood sample to measure their HbA1c.
Results
Of all participants of the KCHS study, 312 patients with diabetes who met the inclusion criteria were identified. Mean (± SD) HbA1c was 8.5% (± 1.8), and 225 (72.1%) of the participants had poor glycemic control. About 45.2% had blood pressure less than 130/80 mmHg, and 24% had FBS lower than 130 mg/dl. Nearly 37.8% of the participants had LDL < 100 mg/dl and 40% TG < 150 mg/dl. Of all participants, 38.5% had undergone retinal examination, 13.8% had their foot examined, and 39.4% had done urine micro-albumin /Cr test. HbA1c level had a statistically significant relationship with gender (P = 0.012), occupation (P = 0.007), nephropathy (P = 0.004) and retinopathy (P < 0.001).
Conclusions
This study showed that less than half of the participants achieved the optimal ADA goals for diabetes care, therefore it is necessary to revise the basic protocols of diabetes care in the region to improve diabetes management.
Keywords: Diabetes care, Diabetes control, Care parameters, Care factors
Introduction
Diabetes is becoming a worldwide concern, as it is estimated that the worldwide number of adults with diabetes will rise to 55% by 2030, and is expected to be mainly in low- and middle-income countries [1, 2]. Every year, over 1% of the urban population in Iran who are above 20 years old are diagnosed with type 2 diabetes [3], and in Khuzestan province, the prevalence of diabetes is 15.3% [1]. This chronic condition poses a significant challenge to public health in Iran due to its widespread occurrence, increasing incidence rate, and substantial economic impact [4].
The risk of mortality among patients with diabetes is twice compared to those without the disease [5]. The case fatality of diabetes in Iran has been on the rise, with the age-standardized mortality rate due to diabetes increasing from 8.7 in 2000 to 11.3 in 2015 [6].
Diabetes is the leading cause of cardiovascular diseases, adult blindness, non-traumatic lower extremity amputations, and end-stage renal disease (ESRD) [7]. In 2009, it was estimated that 49% of the average per capita medical cost for Iranian patients diagnosed with diabetes was related to complications arising from this condition [6]. Hence, currently managing these microvascular and macrovascular complications of chronic hyperglycemia is a major problem [8].
There are many clinical trials showing that optimal glycemic control reduces the diabetes complications [8]. Prediabetes and diabetes care involve lifestyle modification, particularly focusing on diet and exercise. In addition, as far as optimal diabetes care is concerned, blood glucose, blood pressure, lipid panel and weight have a preventive role in diabetes-induced damage to eyes, kidneys, heart and the nervous system [9]. American diabetes association (ADA) standards of care in diabetes recommends screening, diagnostic, and therapeutic measures that are known to improve health outcomes of people with diabetes and reduce their complications and comorbidities [10]. Studies, however, have demonstrated low adherence to ADA-recommendations for diabetes care in United States, and reported adherence rates of 29.5%, 24.6%, 12.8% and 3.6% for biannual HbA1C testing, annual lipid panel, annual foot exams, and annual dilated eye exams, respectively [11]. In 2019, a cross-sectional study was conducted in Iran to evaluate the quality of care for patients with type 2 diabetes based on the HbA1c. The average HbA1c was significantly different among people with different educational attainments, types of occupations, places of residence, and periods of diabetes. Moreover, 66% of the studied subjects had high HbA1c, indicating inadequate diabetes control and poor quality of care in Iran [12].
Despite the rising prevalence of diabetes and its permanent complications and serious co-morbidities, adherence to recommended standards for care of the patients in most Asian countries is still below-par. Evaluating the quality of care for diabetes patients can highlight the importance of improvement, provide a benchmark for the following changes in long term, and be an impetus for health advancement. Many developed countries have diabetes registries and public health databases for regular measurement of the quality of diabetes care [13].To the best of our knowledge, there are no such data sets derived from a specific database in Iran. Moreover, there is no such data available in Khuzestan province (in the southwest of Iran). This can create significant challenges in designing and implementing effective public health interventions aimed at reducing the burden of diabetes in the region.
To address this issue, it is crucial to conduct further research and gather comprehensive data on the management of diabetes in Khuzestan. By doing so, we can develop evidence-based strategies to improve diabetes care parameters and ensure that individuals living with diabetes receive the best possible care. Therefore, we aimed to measure the principal diabetes care parameters to recognize the main deficits in care for patients with diabetes in the region according to decrease diabetes burden.
Methods
Study design and population
This cross-sectional study was performed based on the data from Khuzestan Comprehensive Health Study (KCHS). KCHS was carried out from October 2016 to November 2018. The participants were selected through a multistage random sampling method to evaluate the health conditions of 30,498 Iranian adults aged 20 to 65 years residing in Khuzestan province, southwest of Iran. First, the health centers and health houses in each county were selected randomly (a total of 27 counties). A total number of 1079 of clusters, including 780 health centers in urban and 299 health houses in rural areas were selected. Educated staff visited house to house to brief the participants-to-be on the study goals and requested them to refer to the specified study sites on the subsequent day. The study sites were health houses, health centers, mosques, or schools in the proximity of the participants’ neighborhood. Fifteen milliliters of blood in the fasting state were obtained from each participant, and the specimen was sent to the reference lab. Fasting blood glucose (FBG), lipid profile, and renal function were all evaluated. The anthropometric data including weight, height, waist circumference (WC), and waist-to-hip ratio were measured and recorded for each case. The sampling method and study design have been published previously [14].
Of all participants, 4673 (15.3%) were identified to have diabetes [1]. The research was approved by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences. In the present study, we invited the known cases of diabetes to complete a checklist about their diabetes condition including diabetes duration, insurance, type of treatment (oral anti-diabetic drug (OAD), insulin injection, or just lifestyle modification), consumption of anti-hypertensive drug, lipid-lowering or cardiovascular support drugs, having a glucometer, doing self-monitoring of blood glucose (SMBG), receiving education about their diabetes, diet consultation, doing diabetes preventative care during last year (retinal examination, foot examination, as well as lipid, creatinine, and urine micro-albumin laboratory data), admission history in the last year, and any emergency visit about their diabetes in the previous year. The procedure was fully explained to the participants. The demographic and anthropometric data such as age, gender, education level, and smoking status were also noted. The checklist was filled out through face-to-face interviews. The inclusion criterion was having type 1 or type 2 diabetes. We excluded patients with gestational diabetes, cancer or any other chronic disease requiring hospital admission in the last year, or inability to complete the consent form due to mental or physical disability. We obtained 2 ml of blood from these patients and collected the samples in Cool boxes at 4 °C. Then, we measured HbA1c within 24 h using Nycocard kits (Nycocard, Norway).
Definition of variables and measurements
Ethnicity was described as Arab, Lor, Fars, and Other. Occupation status was defined as a full-time job, part-time job, housewife, student, and unemployed. Having an FBG level of 126 mg/dl or higher, and/or taking anti-diabetic medications, and/or self-reported diabetes indicated diabetes. Body mass index (BMI) was calculated by dividing weight (kg) by the square of the height (m). BMI was classified to: less than 25 kg/ m2 (normal weight), between 25 and 29.9 kg/m2 (overweight), and 30 kg/m2 or higher (obese). Abdominal obesity was defined as a WC of 90 cm or higher for both men and women [15]. Self-monitoring of blood glucose (SMBG) intervals was requested (daily, weekly, monthly, and other).
Hypertension (HTN) was characterized as systolic blood pressure (SBP) equal to or higher than 130 mmHg, and/or diastolic blood pressure (DBP) equal to or higher than 80 mmHg, and/or taking anti-hypertensive drugs [16].
Education on diabetes and its related care was defined as attending any individual or group educational course (face-to-face or virtual) at any public or private institute.
Diabetes complications included myocardial infarction (MI), angina, heart failure, stroke, sensory neuropathy, diabetic foot ulcer (DFU) or amputation, retinopathy [proliferative (PDR) or non-proliferative (NPDR)], nephropathy (micro-albuminuria, macro-albuminuria, and dialysis), peripheral arterial disease (PAD) or any revascularization procedure.
Emergency condition was considered as blood glucose ≤ 70 mg/dl, and poor glycemic control was defined as HbA1c ≥ 8%[17].
Decision cycle for patient-centered glycemic management in type 2 diabetes included different items such as medications, vaccinations, behavioral factors, social life assessment, periodic physical examinations, laboratory tests, and lifestyle modifications to prevent complications and improve quality of life [18]. Diabetes care was assessed by mandatory parameters including HbA1c ≤ 7%, LDL cholesterol ≤ 100 mg/dl, SBP < 130 mmHg and/or DBP < 80 mmHg, and optional parameters including annual examination of retina and/or foot and/or urine micro albumin, diet consultation, diabetes education, SMBG, physical activity, and influenza vaccination [19]. Optimal diabetes care was considered as having mandatory parameters and/or optional factors.
Statistical Analysis
Sample size was calculated according to the KCHS study [1] using Med Calc statistical software (confidence interval: 95%, precision: 4%). Based on this method, we required 310 cases. Out of a total of 30,504 participants who were screened in the first study, 4,673 were identified as known diabetes patients. However, 1038 of them were excluded from the present study. There remained 3635 patients, but most of them or their relatives were not willing to participate in the study after discussion with them. Eventually, 834 subjects were recruited, but 522 refused to give blood samples during the initial recruitment stage. Therefore, laboratory data, including the main variable (HbA1c), was available for only 312 subjects (Fig. 1). Consequently, the questionnaire was completed by these 312 diabetics who had signed written informed consent forms. The sampling method was convenience sampling approach.
Fig. 1.
Diagram of selecting participants
Data were analyzed in SPSS version 20, using mean ± SD to describe continuous data, and frequency and percentage to represent categorical data. The statistical tests applied for this aim were independent t-test, chi-square test, and linear regression analysis. P value ≤ 0.05 was considered statistically significant.
Results
Of all participants of KCHS study, we identified 312 patients with diabetes who met the inclusion criteria, signed our consent form and completed the checklist. Some patients or their relatives denied completion of the questionnaire or giving blood samples.
Of the 312 recruited subjects, 215 (68.9%) were female. The mean (± SD) age of the participants was 52.8(± 7.6) years. The participants had had diabetes for about 7.8 ± 7.10 years (Mean ± SD). Two hundred and forty-six (78.8%) of the cases had a BMI > 25 kg/m2 and were overweight. According to Table 1, the vast majority of the participants (96.8%) were either illiterate or without a high school diploma. More than half of the participants were unemployed (including housewives and students) 263(84.3%). Most of the patients)86.5%) had one type of insurance coverage. Table 1 demonstrates the baseline characteristics of all participants.
Table 1.
Baseline characteristics of all participants
| Variable | All patients (n = 312) |
Women N (%) |
Men N (%) |
P-value |
|---|---|---|---|---|
| Age (Year) | 52.8 ± 7.6 | 52.5 ± 7.9 | 53.5 ± 6.8 | 0.46 |
| Diabetes duration (Year) | 7.8 ± 7.10 | 7.4 ± 6.3 | 8.7 ± 8.3 | 0.26 |
| BMI (kg/m2) | 28.9 ± 4.8 | 29.7 ± 5.0 | 27.2 ± 3.8 | < 0.001* |
| Waist circumference (cm) | 99.2 ± 11.6 | 99.9 ± 12.2 | 97.8 ± 10.1 | 0.22 |
| Education | ||||
| Illiterate | 113 (36.2) | 95 (30.4) | 18 (5.8) | < 0.001* |
| Primary school | 117 (37.5) | 80 (25.6) | 37 (11.9) | |
| High school | 43 (13.8) | 21 (6.7) | 22 (7.1) | |
| Diploma | 29 (9.3) | 16 (5.1) | 13 (4.2) | |
| University degree | 10 (3.2) | 7 (2.2) | 3 (1.0) | |
| Marital status | ||||
| Single | 36 (11.5) | 36 (16.8) | 0 | < 0.001* |
| Married | 276 (88.5) | 179 (83.2) | 97 (100) | |
| Ethnicity | ||||
| Fars | 52 (16.7) | 34 (15.8) | 18 (18.5) | 0.37 |
| Arab | 199 (63.8) | 134 (62.3) | 65 (67.05) | |
| Lor | 52 (16.7) | 39 (18.2) | 13 (13.4) | |
| Other | 9 (2.9) | 8 (3.7) | 1 (1.05) | |
| Occupation status | ||||
| Full time | 22 (7.1) | 2 (1.1) | 20 (20.6) | < 0.001* |
| Part-time | 27 (8.7) | 3 (1.4) | 24 (24.7) | |
| Housekeeper or student | 200 (64.1) | 195 (90.6) | 5 (5.1) | |
| Unemployed | 63 (20.2) | 15 (6.9) | 48 (49.6) | |
| Insurance coverage | ||||
| No | 42 (13.5) | 29 (13.4) | 13 (13.4) | 0.98 |
| Yes | 270 (86.5) | 186 (86.6) | 84 (86.6) | |
| Current treatment type | ||||
| Oral hypoglycemic agent | 253 (81.1) | 175 (81.5) | 78 (80.4) | 0.57 |
| Insulin | 26 (8.3) | 16 (7.4) | 10 (10.3) | |
| Oral hypoglycemic agent + Insulin | 25 (8) | 17 (7.9) | 8 (8.2) | |
| Life-style modification | 8 (2.6) | 7 (3.2) | 1 (1.1) | |
| Family history of diabetes | ||||
| Yes | 198 (63.5) | 135 (62.8) | 63 (64.9) | 0.82 |
| No | 109 (34.9) | 77 (35.8) | 32 (32.4) | |
| Does not know | 5 (1.6) | 3 (1.4) | 2 (2.7) | |
| Past medical history | ||||
| Hypertension | ||||
| Yes | 157 (50.3) | 120 (55.8) | 37 (38.1) | 0.004* |
| No | 155 (49.7) | 95 (44.2) | 60(61.9) | |
| Hyperlipidemia | ||||
| Yes | 186 (59.6) | 138 (64.2) | 48 (49.4) | 0.014* |
| No | 126 (40.4) | 77 (35.8) | 49 (50.6) | |
| Hypoglycemia history | ||||
| Yes | 86 (27.6) | 58 (26.9) | 28 (28.8) | 0.73 |
| No | 226 (72.4) | 157 (73.1) | 69 (71.2) |
*p value ≤ 0.05
The mean ± SD of some participants’ laboratory data were as follows: FBS (205.5 ± 89.4 mg/dl), LDL (117.5 ± 51 mg/dl), Serum creatinine (0.98 ± 0.3 mg/dl), AST (18.7 ± 8 unit/L), ALT (20.1 ± 9.8 unit/L), and hemoglobin (13.3 ± 1.5 g/dl).
More than two-thirds of the cases were on oral agent therapy for their diabetes control (81.1%).
The most common past medical history in patients was hyperlipidemia (59.6%). The most common diabetes complication was diabetic neuropathy (48.1%) with a predominance of paresthesia symptom. Baseline diabetes complications in different genders are shown in Table 2.
Table 2.
Baseline diabetes complications based on gender
| Diabetes complications | All patients (n = 312) |
Women N (%) |
Men N (%) |
P-value |
|---|---|---|---|---|
| MI | ||||
| Yes | 22 (7.1) | 9 (4.1) | 13 (13.4) | 0.003* |
| No | 290 (92.9) | 206 (95.9) | 84 (86.6) | |
| Heart failure | ||||
| Yes | 29 (9.3) | 20 (9.3) | 9 (9.2) | 0.995 |
| No | 283 (90.7) | 195 (90.7) | 88 (90.8) | |
| Angina | ||||
| Yes | 36 (11.5) | 26 (12.1) | 10 (10.3) | 0.648 |
| No | 276 (88.5) | 189 (87.9) | 87 (89.7) | |
| Stroke | ||||
| Yes | 7 (2.2) | 4 (1.8) | 3 (3.1) | 0.496 |
| No | 305 (97.8) | 211 (98.2) | 94 (96.9) | |
| Neuropathy | ||||
| Yes | 150 (48.1) | 108 (50.2) | 42 (43.3) | 0.255 |
| No | 162 (51.9) | 107 (49.8) | 55 (56.7) | |
| DFUa | ||||
| Yes | 17 (5.4) | 7 (3.2) | 10 (10.3) | 0.011* |
| No | 295 (94.6) | 208 (96.8) | 87 (89.7) | |
| Foot amputation | ||||
| Yes | 1 (0.3) | 0 (0) | 1 (1.1) | 0.136 |
| No | 311 (99.7) | 215 (100) | 96 (98.9) | |
| PADb | ||||
| Yes | 3 (1) | 2 (0.9) | 1 (1.1) | 0.933 |
| No | 309 (99) | 213 (99.1) | 96 (98.9) | |
| Nephropathy | ||||
| Yes | 21 (6.7) | 15 (6.9) | 6 (6.2) | 0.758 |
| No | 291 (93.3) | 200 (93.1) | 91 (93.8) | |
| Retinopathy | ||||
| -NPDRc | ||||
| Yes | 43)13.7) | 31(14.4) | 12 (12.4) | 0.627 |
| No | 269 (86.3) | 184 (85.6) | 85 (87.6) | |
| - PDRd | ||||
| Yes | 24(7.7) | 16 (7.4) | 8 (8.2) | 0.805 |
| No | 288 (92.3) | 199 (92.6) | 89 (91.8) | |
|
Neuropathy symptoms Paresthesia |
||||
| Yes | 193 (61.9) | 139 (64.7) | 54 (55.7) | 0.1 |
| No | 119 (38.1) | 76 (35.3) | 43 (44.3) | |
| Hot or cold sensation in feet | ||||
| Yes | 163 (52.2) | 119 (55.3) | 44 (45.4) | 0.102 |
| No | 149 (47.8) | 96 (44.7) | 53 (54.6) | |
| Slipper falling off the feet | ||||
| Yes | 56 (17.9) | 36 (16.7) | 20 (20.6) | 0.409 |
| No | 256 (82.1) | 179 (83.3) | 77 (79.4) |
a: Diabetic Foot Ulcer, b: Peripheral Arterial Disease, c: Non-proliferative Diabetic Retinopathy, d: Proliferative Diabetic Retinopathy
*p value ≤ 0.05
One year prior to the commencement of the study, 41 patients (13.1%) were admitted in the hospital due to their diabetes of whom, 29 (69%) were admitted for glycemic control, and 13 (31%) were admitted due to diabetes complications. The mean ± SD of the admission duration in these patients was 7.27 ± 7.9 days.
Of all subjects, 28 (7.7%) were hospitalized due to hypoglycemia. The mean hospitalization rate due to hypoglycemia in these patients had been (1.39 ± 1.8) times in the last 6 months.
Mandatory diabetes care parameters are described in Table 3. According to this table, the mean (± SD) HbA1c was 8.5% (± 1.8), and 225 (72.1%) of the participants had poor glycemic control. Furthermore, only 8 (2.6%) patients met all criteria for optimal diabetes care while 254 (81.4%) had only one of the mentioned criteria.
Table 3.
Mandatory diabetes care parameters
| Parameter | Overall | Women | Men | P-value |
|---|---|---|---|---|
| HbA1c (%) | 8.5 ± 1.8 | 8.48 ± 1.8 | 8.62 ± 1.75 | 0.44 |
| HbA1c measurement interval | ||||
| Once a year | 37 (11.3) | 28 (13.04) | 9 (9.27) | 0.025* |
| Twice a year | 51 (16.4) | 43 (20) | 8 (8.23) | |
| ≥ 3 times a year | 57 (18.6) | 41 (19.06) | 16 (16.5) | |
| Fail to do in a year | 167 (53.7) | 103 (47.9) | 64 (66) | |
| BP Measurement interval | ||||
| Once a year | 58 (18.6) | 41 (19.06) | 17 (17.55) | 0.991 |
| 2–3 times a year | 82 (26.3) | 56 (26.04) | 26 (26.8) | |
| ≥ 4 times a year | 124 (39.7) | 85 (39.55) | 39 (40.2) | |
| Never | 48 (15.4) | 33 (15.35) | 15 (15.45) | |
| Blood pressure (mmhg) | ||||
| Systolic BP | 126.5 ± 16.2 | 126.3 ± 15.7 | 127.03 ± 17.3 | 0.91 |
| Diastolic BP | 81.8 ± 12.2 | 82.07 ± 12.7 | 81.2 ± 11.2 | 0.51 |
| Lipid profile test in last year | ||||
| Yes | 180 (57.7) | 122 (56.75) | 58 (59.8) | 0.097 |
| No | 122 (39.1) | 83 (38.6) | 39 (40.2) | |
| Does not remember | 10 (3.2) | 10 (4.65) | 0 (0) | |
| LDL (mg/dl) | ||||
| < 100 | 118 (37.8) | 74 (34.7) | 44 (45.8) | 0.052* |
| 100–129 | 83 (26.6) | 55 (25.9) | 28 (28.9) | |
| 130–159 | 49 (15.7) | 35 (16.7) | 14 (14.5) | |
| ≥ 160 | 62 (19.9) | 48 (22.7) | 10 (10.8) | |
| HDL (mg/dl) | ||||
| < 40 | 101 (30.7) | 51 (23.7) | 50 (51.5) | < 0.001* |
| 40–49 | 92 (29.6) | 68 (31.6) | 24 (24.8) | |
| 50–59 | 62 (20.7) | 48 (22.35) | 14 (14.4) | |
| ≥ 60 | 57(19) | 48 (22.35) | 9 (9.3) | |
| Triglyceride(mg/dl) | ||||
| < 150 | 124 (40) | 87 (40.5) | 39(39.2) | 0.698 |
| 150–199 | 67 (21) | 48 (22.3) | 19 (19.5) | |
| 200–399 | 103 (33.2) | 69 (32.09) | 34 (34.1) | |
| ≥ 400 | 18 (5.8) | 11 (5.1) | 7(7.2) | |
*p value ≤ 0.05
Of all subjects, 139 (44.6%) used anti-hypertensive drugs, and 161 (51.6%) took anti-lipid drugs, especially statins. One hundred and seventy-one cases (54.8%) had systolic blood pressure (SBP) of more than 130 mmHg or a diastolic blood pressure (DBP) of more than 80 mmHg, and 75 (24%) had FBS lower than 130 mg/dl.
Optional diabetes care parameters are shown in Table 4. Statin consumption and physical activity were the two variables which had a statistically significant relationship with gender. Women used statin more than men did (51.2% VS. 36.1%) (P = 0.013). Furthermore, men (31.9%) had significantly more physical activity than women did (21.4%) (P = 0.045).
Table 4.
Optional diabetes care parameters
| Variables | All patients (n = 312) |
Women N (%) |
Men N (%) |
P-value |
|---|---|---|---|---|
| Retinal examination | ||||
| Within last year | 120 (38.5) | 88 (40.9) | 32 (32.98) | 0.613 |
| 1–2 years ago | 55 (17.6) | 36 (16.8) | 19 (19.6) | |
| > 2 years ago | 38 (12.2) | 25 (11.6) | 13 (13.4) | |
| Never | 99 (31.7) | 66 (30.7) | 33 (34.02) | |
| Neurologic and vascular foot examination within last year | ||||
| Yes | 43 (13.8) | 32 (14.8) | 11 (11.4) | 0.649 |
| No | 251 (80.4) | 170 (79.1) | 81 (83.5) | |
| Does not remember | 18 (5.8) | 13 (6.1) | 5 (5.1) | |
| Urine micro-albumin/Cr test in last year | ||||
| Yes | 123 (39.4) | 85 (39.5) | 38 (39.1) | 0.835 |
| No | 180 (57.7) | 123 (57.2) | 57 (58.8) | |
| Does not remember | 9 (2.9) | 7 (3.3) | 2 (2.1) | |
| Serum creatinine test in last year | ||||
| Yes | 77 (24.7) | 55 (25.6) | 22 (22.7) | 0.429 |
| No | 219 (70.2) | 147 (68.3) | 72 (74.2) | |
| Does not remember | 16 (5.1) | 13 (6.1) | 3 (3.1) | |
| ECG in last year | ||||
| Yes | 173 (55.4) | 125 (58.1) | 48 (49.49) | 0.115 |
| No | 135 (43.3) | 86 (40) | 49 (50.51) | |
| Does not remember | 4 (1.3) | 4 (1.9) | 0 (0) | |
| Statin use | ||||
| Yes | 145 (46.5) | 110 (51.2) | 35 (36.1) | 0.013* |
| No | 167 (53.5) | 105 (48.8) | 62 (63.9) | |
| Aspirin use | ||||
| Yes | 98 (31.4) | 63 (29.3) | 35 (36.1) | 0.232 |
| No | 214 (68.6) | 152 (70.7) | 62 (63.9) | |
| Having a glucometer | ||||
| Yes | 129 (41.3) | 81 (37.7) | 48 (49.49) | 0.050* |
| No | 183 (58.7) | 134 (62.3) | 49 (50.51) | |
| SMBG interval | ||||
| Daily | 22 (7.1) | 16 (7.4) | 6 (6.2) | 0.113 |
| Weekly | 58 (18.6) | 38 (17.6) | 20 (20.6) | |
| Monthly | 37 (11.9) | 24 (11.2) | 13 (13.4) | |
| Other | 13 (4.2) | 5 (2.3) | 8 (8.2) | |
| Never | 182 (58.2) | 132 (61.5) | 50 (51.6) | |
| Education about diabetes | ||||
| Yes | 66 (21.2) | 40 (18.6) | 26 (26.8) | 0.101 |
| No | 246 (78.8) | 175 (81.4) | 71 (73.2) | |
| Education about HbA1C | ||||
| Yes | 85 (27.2) | 58 (26.9) | 27 (27.8) | 0.875 |
| No | 227 (72.8) | 157 (73.1) | 70 (72.2) | |
| Diet consultation | ||||
| Yes | 75 (24) | 59 (27.4) | 16 (16.5) | 0.061 |
| No | 237 (76) | 156 (72.6) | 81 (83.5) | |
| Annual influenza vaccination | ||||
| Yes | 55 (17.6) | 33 (15.3) | 22 (22.7) | 0.116 |
| No | 257 (82.4) | 182 (84.7) | 75 (77.3) | |
| Weight measurement in each visit | ||||
| Yes | 163 (52.2) | 118 (54.9) | 45 (46.4) | 0.165 |
| No | 149 (47.8) | 97 (45.1) | 52 (53.6) | |
| Physical Activity | ||||
| Yes | 77 (24.7) | 46 (21.4) | 31 (31.9) | |
| No | 235 (75.3) | 169 (78.6) | 66 (68.1) | 0.045* |
*p value ≤ 0.05
Table 5 demonstrates the results of the linear regression analysis based on diabetes care parameters. Gender and hypertension had a significant correlation with LDL level. LDL level in women was 22.5 units more than that in men (P = 0.048), and LDL level in hypertensive patients was 12.7 units lower than that in non-hypertensive cases (P = 0.032).
Table 5.
Linear regression models based on diabetes care parameters
| Parameters | LDL | Systolic Blood Pressure | Diastolic Blood Pressure | HbA1C | ||||
|---|---|---|---|---|---|---|---|---|
| Coef. (SE) | P-value | Coef. (SE) | P-value | Coef. (SE) | P-value | Coef. (SE) | P-value | |
| Age | -0.3 (0.4) | 0.451 | 0.37 (0.13) | 0.004* | 0.25 (0.1) | 0.010* | 0 (0.01) | 1.00 |
| Gender (RC = Male) | 22.55 (11.36) | 0.048* | 2.07 (3.55) | 0.561 | 0.68 (2.73) | 0.804 | -1.03 (0.41) | 0.012* |
| Diabetes duration | 0.59 (0.41) | 0.148 | 0.1 (0.13) | 0.423 | -0.04 (0.1) | 0.696 | -0.01 (0.01) | 0.665 |
| BMI | -0.34 (1.04) | 0.742 | -0.12 (0.33) | 0.717 | 0.22 (0.25) | 0.386 | -0.02 (0.04) | 0.599 |
| Waist circumference | 0.18 (0.42) | 0.671 | 0.14 (0.13) | 0.289 | 0 (0.1) | 0.978 | 0 (0.02) | 0.967 |
| Marital Status (RC = single) | 2.41 (9.1) | 0.791 | 0.74 (2.85) | 0.794 | -1.32 (2.19) | 0.546 | 0.16 (0.33) | 0.63 |
| Insurance coverage (RC = yes) | 2.86 (8.19) | 0.728 | 3.69 (2.56) | 0.15 | 5.73 (1.97) | 0.004 | 0.3 (0.3) | 0.318 |
| Education Level (RC = University degree) | ||||||||
| Illiterate | -10.15 (16.53) | 0.54 | -1.4 (5.17) | 0.787 | -1.22 (3.97) | 0.759 | -0.51 (0.6) | 0.391 |
| Primary school | -17.26 (16.13) | 0.285 | -2.74(5.04) | 0.587 | -2.42 (3.88) | 0.533 | -0.62 (0.58) | 0.289 |
| High school | 2.96 (16.62) | 0.859 | 1.01 (5.2) | 0.846 | -2.58(4) | 0.519 | -0.69(0.6) | 0.254 |
| Diploma | -16.3 (17.75) | 0.359 | -6.55 (5.55) | 0.239 | -5.58 (4.27) | 0.192 | -0.05 (0.64) | 0.938 |
| Occupation (RC = Unemployed or retired) | ||||||||
| Full time | -16.19 (12.25) | 0.187 | -0.27 (3.83) | 0.944 | 1.19 (2.94) | 0.685 | -0.11 (0.44) | 0.806 |
| Part time | -0.7 (11.37) | 0.951 | 1.4 (3.56) | 0.693 | 2.92 (2.73) | 0.286 | 0.18 (0.41) | 0.669 |
| Housewife or student | -7.74 (10.65) | 0.468 | -5.11 (3.33) | 0.126 | -1.93 (2.56) | 0.451 | 1.06 (0.39) | 0.007* |
| Physical activity (RC = yes) | 2.21 (6.34) | 0.728 | 1.25 (1.98) | 0.528 | 1.92 (1.52) | 0.21 | 0.06 (0.23) | 0.798 |
| Heart Disease (RC = yes) | 7.26 (7.28) | 0.319 | 0.56 (2.28) | 0.805 | -0.16 (1.75) | 0.929 | 0.42 (0.26) | 0.114 |
| Retinopathy (RC = yes) | 5.26 (7.16) | 0.463 | 0.59 (2.24) | 0.792 | -0.05 (1.72) | 0.975 | -1.51 (0.26) | < 0.001* |
| Neuropathy (RC = yes) | -3.36 (5.78) | 0.562 | 1.99 (1.81) | 0.272 | 0.2 (1.39) | 0.885 | -0.13 (0.21) | 0.549 |
| Nephropathy (RC = No) | -11.12 (16.72) | 0.506 | 6.11 (5.23) | 0.244 | 7.55 (4.02) | 0.061 | 1.78 (0.61) | 0.004* |
RC: Reference Category, Coef: coefficient, SE: standard error, BMI: Body Mass Index
* p value ≤ 0.05
There was a significant relationship between age and systolic blood pressure. A one-year increase in age elevated the systolic BP by about 0.37 mmHg (P = 0.004). Parallel with this result, diastolic blood pressure had a significant correlation with age. For every one-year increase in age, the diastolic BP rose by about 0.25 mmHg (P = 0.01).
HbA1c level had a statistically significant relationship with gender (P = 0.012), occupation (P = 0.007), nephropathy (P = 0.004), and retinopathy (P < 0.001). HbA1c level in women was 1.03% lower than that in men. Furthermore, the HbA1c level in housewives and students was 1.06% more than that in patients who were retired or unemployed. HbA1c level in patients with diabetic nephropathy was 1.78% more than that in patients without this complication. Moreover, Hba1c level in patients with diabetic retinopathy was 1.51% more than that in diabetic patients without retinopathy.
Discussion
This cross-sectional study explored the diabetes care parameters in patients with diabetes, in Khuzestan province (Iran). Rates of adherence to individual diabetes care procedures were usually poor, and only 2.6% of the study population adhered to all ADA-recommended diabetic care parameters (HbA1c ≤ 7 ‘and’ LDL < 100 ‘and’ BP < 130/80), which is consistent with and even lower than the results obtained in other studies [11]. However, 81.4% of the participants had one of these items controlled: (HbA1c ≤ 7’or’ LDL < 100 ‘or’ BP < 130/80). In the present study, of all participants, 72.1% had poor glycemic control, and the mean ± SD HbA1c was 8.5 ± 1.8. More than half of them (57.7%) had had lipid profile test in the last year of whom, 37.8% had LDL < 100 mg/dl and 40% TG < 150 mg/dl. Besides, 75 cases (24%) had FBS lower than 130 mg/dl.
Mean HbA1c level in the present study was 8.5 ± 1.8 (%), which is lower than that in some other studies [5, 20]. In Fukunaga et al., the participants were older than our participants, and in McLendon et al., the inclusion criteria included HbA1c more than 8%. However, in another study conducted in Vietnam which included 1631 cases, the mean HbA1c was 7.9 ± 1.8%, which is nearly compatible with our study [21]. Mirahmadzadeh et al. reported a mean of 7.48 ± 0.06% for HbA1c, which is lower than ours. These discrepancies could be due to educational strategies or geographical and cultural differences.
In our study, 72.1% of the patients had poor glycemic control (HbA1c > 8). After comparing this result with other studies, we found that the cut-off point for poor glycemic control was different in several studies. A review conducted in northern Iran in 2016 demonstrated that 73.3% of the study patients had inadequate glycemic control considering HbA1c ≥ 7 [22]. The rate reported by Babaniamansour et al. in Iran was 37% based on a cut-off point of HbA1c:8–10 [23]. HbA1c ≤ 7% was reported in 39.8% of the cases in another survey in Iran [24]. Thy Nguyen et al. reported 39% with HbA1c > 8, which is comparable with our report [21]. In Nagpal et al.’s study in Delhi, 33.5% had HbA1c more than 10, which is almost double that of the present study (17.9%) [13]. Moreover, in Delavari et al.’ study, which was done in Iran in 2009, 97.4% of the participants had HbA1c more than 9 [25]. Another study in Brazil showed that only 26% of their participants had an HbA1c < 7% [26]. In Gebrie et al., 73.5% of their cases had HbA1c ≥ 7 [8]. More than half of our patients’ HbA1c was in the range of 7–8%, and an overall comparison with these studies seems to show better HbA1c control in our population.
HbA1c test can provide a highly probable estimation of diabetes control in the long term; therefore, it is one of the most valuable tests to evaluate patients with diabetes. ADA guidelines have recommended it at least every 3 months. In this study, about half of the patients (46.3%) had measured their HbA1c level, 1–3 times a year; which was an acceptable rate in comparison with other studies. None of the enrolled participants in Gudina et al.’ study in Ethiopia had checked their HbA1c level within the last year [27]. In another study in China, HbA1c was measured 1.8 ± 1.3 times per year [28]. Delavari et al. mentioned that 6.4% of their patients had done the HbA1c test in the last year [25]. The differences between our results and those obtained in previous studies may be the result of various cultural behaviors or educational patterns, different types of insurance coverage, or the availability of the test. However, the HbA1c test is highly sensitive to its implementation method, which may not be the same in these studies. Besides, in another investigation in California, HbA1c was measured twice a year in 40.4% of the participants, which is consistent with our report [11].
We discovered that 75 cases (24%) in our study had FBS lower than 130 mg/dl. Weledegebriel and colleagues reported that 44.1% of their participants had FBS: 70–140 [7]. This discrepancy may be because of some contributing variables. For instance, their population had about 3 times more physical activity than ours (60.2% vs. 24%), lower BMI, and different distribution of type 1 and type 2 diabetes.
In the present evaluation, 59.6% of the subjects had hyperlipidemia at the baseline. More than half of all participants (57.7%) had done a lipid profile test in the last year, of whom 37.8% had LDL < 100 mg/dl and 40% TG < 150 mg/dl. Lipid profile was evaluated once a year in the following studies: less than 5% in Ethiopia [27], 24.6% in Iran [25] and 49.3% in the United States [11]. Besides, according to a study in India, 22.3% of the patients did the lipid test twice a year. In their study, LDL < 100 was reported in 32.8% of the cases and TG < 150 in 41.7% of them, which is compatible with the present study [13]. Overall, the status of the annual lipid profile test in our study was better than that in some other mentioned studies.
The vast majority (84.6%) of the patients in our study had measured their blood pressure once a year in the healthcare provider’s office. Of all participants, 50.3% were hypertensive and 44.6% took anti-hypertensive drugs, of whom 54.8% had uncontrolled blood pressure (> 130/80mmhg). Our hypertensive patients were to some extent comparable with those in Gudina et al.’s study [27]. Also, in their study, 64.1% of the patients had blood pressure higher than 130/80 mmHg. The rate of hypertensive patients in our study was nearly close to that in a previous study in Iran (45%) [25], but lower than the rate reported by Thy Nguyen et al. (78.4%) [21]. The probable reason for this difference may be the older age and the longer duration of diabetes in their participants in comparison with ours. In accordance with the present study, blood pressure > 140/90 mmHg was observed in 57.3% of the participants in Nagpal et al.’s study [13]. Moreover, concerning checking blood pressure in the health care provider’s office, Delevry et al. reported a higher number of patients checking their blood pressure within the last year in comparison with our participants (94.1% vs. 84.6%), although these two numbers were close to each other [11].
Screening of diabetes complications is an important part of diabetes care since all diabetes management practices follow a common goal which is complication prevention. Our findings in this regard showed that of all participants, 38.5% had done a retinal examination, 13.8% had their foot examined, 39.4% had done a urine micro-albumin /Cr test, and 55.4% had been evaluated by ECG within the last year. Unfortunately, as it was predictable, feet are still neglected by patients with diabetes all over the world. The low rate of annual foot screening was consistent with that in all other evaluated studies [11, 13, 25, 27]. The rate of annual retinal examination in the present study was in accordance with other studies [11, 25, 27] and more than that reported by Nagpal et al. in Delhi [13]. Regarding the annual urine micro-albumin /Cr test, our findings showed a higher rate in comparison with Nagpal et al. (39.4% vs.1.2%) [13] and a lower rate compared with Gudina study (39.4% vs.66%) [27]. These differences could be explained by cultural, educational or geographical diversity of the participants.
Self-monitoring of blood glucose (SMBG) is a commonly used method to measure changes in blood sugar levels. It is a valuable tool for monitoring and managing diabetes. More than half of the patients (58.2%) in our study did not have SMBG for their diabetes control, which is in line with some other studies [21] and lower than the rates reported in Ethiopia (94.5%) and Delhi (85%) [13, 27]. Our findings were predictable since 58.7% of our patients did not have glucometer. According to Pleus et al., people with type 2 diabetes have poor adherence to SMBG: more than 70% had no SMBG in china, and low SMBG in India and Brazil [26]. In line with the guidelines, the frequency of SMBG should be individualized, but its overall rate is low in different countries, which could be attributed to financial issues, low education of patients about self-management, and some cultural or behavioral barriers such as needle phobia.
Along with previous reports, in our study, 78.8% of diabetics were identified as overweight with a BMI exceeding 25 kg/m2, which is particularly notable in women [29, 30]. However, in the Weledegebriel et al.study, only 16.1% of the patients had a BMI > 25, which may be due to more physical activity of their patients in comparison with ours (60.2% vs.24%) [7].
Before any recommendation for diabetes management by anti-hyperglycemic drugs, lifestyle modification is a cornerstone of diabetes control. Lifestyle modification emphasizes a healthy diet and physical activity as the most important items. In the present survey, only about 24% of the participants had consultation with a dietician or had physical activity in their daily routine. The rate of diet consultation in our study was lower than that in Nagpal et al. (56%) [13]. A previous Iranian study reported low adherence to the Mediterranean diet (5.4%) and to physical activity (about 21%) [24]. Weledegebriel et al. found that 60.2% of their patients had physical activity, which is about 3 times more than that of our participants. This difference could be explained by the fact that their population was younger compared with ours and that 33.2% of their cases were farmers which involves high physical activity [7]. Gebrie et al. demonstrated that the majority of their patients (66.7%) did not have sufficient physical activity, which is similar to our results [8] and could be as a result of lack of motivation and self-efficacy. Moreover, in the present study, men (31.9%) had remarkably more physical activity than women (21.4%), which could be attributed to the fact that in the Iranian context, compared with women, men have more access to facilities to exercise. Additionally, Llamas-Saez et al. illustrated that males with diabetes reported more physical activity than females did [31].
Certainly, education on any subject leads to its promotion, and diabetes management is no exception. In our study, only 21.2% of the cases had received education on diabetes control. In another research, 36.1% of the participants were not educated on diabetes [7]. This discrepancy might be explained by the fact that their patients were younger than our participants and may have used more online options such as social media for self-education.
According to the linear regression analysis, HbA1c level had a statistically significant relationship with gender (P = 0.012), occupation (P = 0.007), nephropathy (P = 0.004) and retinopathy (P < 0.001). Positive correlation with HbA1c was identified for gender in a study in Germany, which is consistent with our study, but their participants were children and adolescents [32]. Huang et al. in Taiwan discovered that the HbA1c levels of men were significantly higher compared with women, which is in agreement with the present study [33]. Another study in Iran showed that glycemic control had a significant correlation with occupation (P = 0.04), which corroborates our results [23]. Meenu and colleagues examined the relationship between levels of HbA1c and lipid profile. They found significant correlations between HbA1c and cholesterol, triglycerides, LDL & VLDL and a reverse relationship with HDL [34]. Meanwhile, another study showed that adherence rates to individual parameters of diabetes care had a significant relationship with age, marital status, smoking, income and private insurance [11].
Our study is significantly worthwhile in that its participants were selected from among residents of a province, not a clinic, which may increase the chance of extending and generalizing the results. Despite the notable strengths of this study, however, it had several limitations such as the cross-sectional design. Future prospective studies are needed to evaluate the care parameters to demonstrate the impact of interventions and draw their causal associations and effective interventions. The unavailability of a larger number of participants in the basic study and their low cooperation limited our study as well. To address low response, future studies could utilize diabetes registries for enrollment to reduce self-selection and recall bias. Sociodemographic and economic factors must be collected through validated questionnaires to allow equity analyses. Medical records can minimize recall and reporting biases. We did not consider some other care indicators in diabetes such as dental health and psychological issues including self-efficacy, uncooperative health behaviors, denial of the disease, inadequate family support, discouraging environment, psychological consultations for handling depression or anxiety, genetic issues, rate of access to services, and admission details. Multivariable regression models should adjust for relevant clinical confounders to clarify independent associations.
It is recommended to develop a data-base for our diabetes clinics in each region to have access to updated and detailed data for future studies with a prospective design. We can use a great recent opportunity “artificial intelligence methods’’ to establish more practical and purposive facilities to increase diabetes care such as educating patients about having access to them via different technologies and promoting self-management of diabetes programs. Developing more diabetes care can reduce diabetes complications and lead to a better quality of life for the growing diabetes population worldwide.
Conclusion
Although HbA1c, blood pressure and lipid profile had been measured frequently in the past year in more than half of our patients, less than half of them had achieved the optimal ADA goals for diabetes care in terms of HbA1C, blood pressure, FBS, LDL and TG levels. Similar to many other countries, the annual screening of diabetes complications, especially in foot care, has been low. Therefore it is necessary to revise the basic protocols of diabetes care in the region to improve diabetes management.
Acknowledgements
This study was conducted with the cooperation of Ahvaz Jundishapur University of Medical Sciences. The authors sincerely thank all participants, Dr. Mahmood Maniati who proofread the earlier versions of the manuscript, and Dr. Zahra Rahimi who provided essential data from KCHS study.
Funding
This study was funded by Ahvaz Jundishapur University of Medical Sciences (D-9611).
Data Availability
Data will be made available on reasonable request.
Declarations
Ethical Approval
The research was approved by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences (No: IR.AJUMS.REC.1396.1054) Ethical issues have been completely observed by the authors.
Conflict of interest
The authors declared that they have no conflict of interest.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Data Availability Statement
Data will be made available on reasonable request.