Poor glycaemic control and its metabolic and demographic risk factors in a Malaysian community-based study

Reidpath, Daniel D.; Soyiri, Ireneous; Jahan, Nowrozy Kamar; Mohan, Devi; Ahmad, Badariah; Ahmad, Mohtar Pungut; Kassim, Zaid; Allotey, Pascale

doi:10.1007/s00038-017-1072-4

Cited by 6 publications

(6 citation statements)

References 37 publications

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“…Even though studies established the influence of glycemic control on the progression of diabetic complications, small proportion of DM populations achieve the target glycemic level [11,12,20,21]. Different factors have contributed to poor glycemic control including age, duration of the disease, type of treatment, patients' perception of health, educational level, occupation, and medication adherence [22][23][24][25]. However, there is limited evidence on how these factors are associated with poor glycemic control in Ethiopia [26,27].…”

Section: Introductionmentioning

confidence: 99%

Rate of glycemic control and associated factors among type two diabetes mellitus patients in Ethiopia: A cross sectional study

et al. 2021

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Objective To assess the rate of glycemic control and associated factors among type 2 diabetes mellitus patients at Dilchora Referral Hospital, Dire Dawa, Eastern Ethiopia. Methods A cross-sectional study was conducted from 13 May to 16 August 2019. Type 2 diabetic patients on follow up at Dilchora Referral Hospital who fulfilled the inclusion criteria of the study were included. Systematic random sampling was used to select study participants. Data was collected by a face-to-face interview and review of medical records. The primary outcome was the level of blood glucose during three consecutive visits. Poor glycemic control was defined as a blood sugar level of more than 154 mg/dL based on the average of measurements from three consecutive visits. Multivariate logistic regression analysis was used to identify determinants of glycemic control. Result A total of 394 participants responded to the interview and were included in the final analysis. The overall prevalence of poor glycemic control was 45.2% (95%CI: 40.6%-50.0%). Patients who were on oral anti-diabetic drug plus insulin had more than two times greater chance of poor glycemic control than patients on oral anti-diabetic drug alone: 2.177(95%CI:1.10–4.29). The odds of poor glycemic control in patients who did not understand the pharmacist’s instructions was two times higher than patients with good understanding of instructions 1.86(95%CI: 1.10–3.13). Patients who had poor level of practice were found to have poor glycemic control: 1.69(95% CI: 1.13–2.55). Conclusion The overall prevalence of poor glycemic control was high among type 2 diabetes patients. Oral anti-diabetic drugs in combination with insulin, lack of understanding of pharmacist’s advice, and poor practice of diabetic patients were significant factors of poor glycemic control. Pharmacists should reassure the understanding of patients before discharge during counseling. Optimization of the dose of antidiabetic medications and combination of oral hypoglycemic agents should be considered.

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Section: Introductionmentioning

confidence: 99%

Rate of glycemic control and associated factors among type two diabetes mellitus patients in Ethiopia: A cross sectional study

et al. 2021

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“…This longitudinal study uses data from the SEACO, a certified health and demographic surveillance system 26 . The data is part of community health surveys (2013 and 2018) conducted by SEACO in Segamat (a semi‐rural community in Johor, Malaysia).…”

Section: Methodsmentioning

confidence: 99%

“…This longitudinal study uses data from the SEACO, a certified health and demographic surveillance system. 26 The data is part of…”

Section: Data Source and Study Designmentioning

confidence: 99%

“…Diabetes is also a common chronic non‐communicable disease (NCD) worldwide, with growing incidence and prevalence 25 . As of 2014, 8.5% of the global adult population lives with diabetes worldwide 26 . The Institute for Public Health in Malaysia reported that the prevalence of known diabetes increased from 8.3% to 9.4%, while the prevalence of known hypertension increased from 13.1% to 15.9% between 2015 and 2019 10,11 .…”

Section: Introductionmentioning

confidence: 99%

“… 25 As of 2014, 8.5% of the global adult population lives with diabetes worldwide. 26 The Institute for Public Health in Malaysia reported that the prevalence of known diabetes increased from 8.3% to 9.4%, while the prevalence of known hypertension increased from 13.1% to 15.9% between 2015 and 2019. 10 , 11 The increasing prevalence of known hypertension and diabetes in Malaysia is expected to increase healthcare utilization for hypertension and diabetes management and prevent complications.…”

Section: Introductionmentioning

confidence: 99%

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Healthcare cost coverage inequality and its impact on hypertension and diabetes: A five‐year follow‐up study in a Malaysian rural community

Folayan,

Fatt,

Cheong

et al. 2024

Health Science Reports

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Background and AimsInequality in health care access is a socioeconomic driver for non‐communicable disease related risk factors. This study examined the inequality trend in healthcare cost coverage (HCC) compared to private health insurance (PHI) coverage, a subtype of HCC, over 5 years. The study will also determine the association between HCC (and PHI) and the status of hypertension and diabetes diagnosis.MethodThe rich‐poor ratio, concentration curve and concentration index were derived to determine the level of inequality. Furthermore, logistic regression was done to determine the association between HCC and the status of hypertension and diabetes.ResultsThe PHI group (rich‐poor ratio: 1.4 [rich: 454, poor: 314] and 2.6 [rich: 375, poor: 142]; concentration index: 0.123 [95% confidence interval, CI: 0.093–0.153] and 0.144 [95% CI: 0.109–0.178] in 2013 and 2018, respectively) has relatively higher inequality compared with the HCC group (rich‐poor ratio: 0.9 [rich: 307, poor: 337] and 1.1 [rich: 511, poor: 475]; concentration index: −0.027 [95% CI: −0.053 to −0.000] and −0.014 [95% CI: −0.033 to 0.006] in 2013 and 2018, receptively). Contrasting to the observation with the HCC group, PHI was associated with higher odds for hypertension (adjusted odds ratio [aOR] = 1.252, p = 0.01, 95% CI: 1.051–1.493) and diabetes (aOR = 1.287, p = 0.02, 95% CI: 1.041–1.590) in 2018.ConclusionOver 5 years, the inequality in PHI coverage remained higher compared with HCC, which suggests that the rich enjoyed private healthcare more. Furthermore, those with PHI were more likely to report known hypertension and diabetes in 2018. It is reasonable to assume that those with PHI are more likely to have earlier diagnoses compared to others and are more likely to be aware of their condition. Policymakers need to identify strategies that can narrow the existing gap in quality and type of service between the private and public health sectors.

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Glycaemic monitoring and control among high-risk patients with type 2 diabetes in Australian general practice during COVID-19

2023

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BackgroundThe COVID-19 pandemic disrupted general practice worldwide, primarily due to public health measures that restricted access to care for chronic diseases, such as type 2 diabetes. These measures disproportionately affected higher risk groups with type 2 diabetes, such as older people and those with obesity. This study aims to identify factors that may have influenced the rates of compliance with testing guidelines and target glycaemic control in Australian general practice settings during the COVID-19 pandemic.MethodsWe used a serial cross-sectional study design of patient record data from general practices representative of the Nepean Blue Mountains Local Health District between 2020 and 2022. Aggregated patient records were analysed to determine percentages of subgroups with a blood glycaemic testing interval consistent with guidelines (≥1 within 15 months) and achieving target glycaemic control (by glycated haemoglobin of ≤7%). Linear regression models were used to test the association between independent and dependent variables, and to generate regression coefficients and 95% CI, corrected for time trends.ResultsOf the average 14 356 patient records per month, 55% were male, 53% had a body mass index (BMI) <30 and 55% were aged 55–74 years. Compliance to testing guidelines slightly decreased (75–73%) but was positively associated with male sex (2.5%, 95% CI 1.7%, 3.4%), BMI≥30 (9.6%, 95% CI 8.8%, 10.4%) and 55–74 years (7.5%, 95% CI 6.6%, 8.5%) and 75 years and over age groups (7.1%, 95% CI 6.2%, 7.9%). Mean percentage of patient records achieving target glycaemic control slightly increased and was negatively associated with male sex (−3.7%, 95% CI −5.2%, –2.2%), but positively associated with 55–74 years (4.5%, 95% CI 3.8%, 5.1%) and 75 years and over age groups (12.2%, 95% CI 4.5%, 20.0%). Compliance to testing guidelines increased with each additional general practice per 10 000 persons (8.4%, 95% CI 4.9%, 11.8%).ConclusionsDuring the COVID-19 pandemic, people with type 2 diabetes in Australia continued to follow glycaemic testing guidelines at the same rate. In fact, there was a slight improvement in glycaemic control among all subgroups of patients, including those at higher risk. These findings are encouraging, but the longer term impact of COVID-19 on type 2 diabetes care is still unclear.

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Poor glycaemic control and its metabolic and demographic risk factors in a Malaysian community-based study

Abstract: There is a critical need for evidence for a better understanding of the mechanisms of the associations between risk factors and glycaemic control.

Cited by 6 publications

References 37 publications

Rate of glycemic control and associated factors among type two diabetes mellitus patients in Ethiopia: A cross sectional study

Rate of glycemic control and associated factors among type two diabetes mellitus patients in Ethiopia: A cross sectional study

Healthcare cost coverage inequality and its impact on hypertension and diabetes: A five‐year follow‐up study in a Malaysian rural community

Glycaemic monitoring and control among high-risk patients with type 2 diabetes in Australian general practice during COVID-19

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