DESIGN: Cross-sectional survey conducted between April and May 2017.
SETTING: Forty public clinics in Malaysia.
PARTICIPANTS: A total of 956 adult patients with T2D and/or hypertension were interviewed.
MAIN OUTCOME MEASURES: Patient experience on SMS was evaluated using a structured questionnaire of the short version Patient Assessment of Chronic Illness Care instrument, PACIC-M11. Linear regression analysis adjusting for complex survey design was used to determine the association of patient and clinic factors with PACIC-M11 scores.
RESULTS: The overall PACIC-M11 mean was 2.3(SD,0.8) out of maximum of 5. The subscales' mean scores were lowest for patient activation (2.1(SD,1.1)) and highest for delivery system design/decision support (2.9(SD,0.9)). Overall PACIC-M11 score was associated with age, educational level and ethnicity. Higher overall PACIC-M11 ratings was observed with increasing difference between actual and expected consultation duration [β = 0.01; 95% CI (0.001, 0.03)]. Better scores were also observed among patients who would recommend the clinic to friends and family [β = 0.19; 95% CI (0.03, 0.36)], when health providers were able to explain things in ways that were easy to understand [β = 0.34; 95% CI (0.10, 0.59)] and knew about patients' living conditions [β = 0.31; 95% CI (0.15, 0.47)].
CONCLUSIONS: Our findings indicated patients received low levels of SMS. PACIC-M11 ratings were associated with age, ethnicity, educational level, difference between actual and expected consultation length, willingness to recommend the clinic and provider communication skills.
METHODS: A cross-sectional analysis of 13 784 medical records from 20 selected public primary care clinics in Malaysia was performed for patients aged ≥30 years old who were diagnosed with hypertension and had at least one visit between 1st November 2016 and 30th June 2019. Multivariable logistic regression adjusted for complex survey design was used to determine the association between process of care and blood pressure (BP) control among the hypertensive patients.
RESULTS: Approximately 50% of hypertensive patients were obese, 38.4% of age ≥65 years old, 71.2% had at least one comorbidity and approximately one-third were on antihypertensive monotherapy. Approximately two-third of the hypertensive patients with diabetic proteinuria were prescribed with the appropriate choice of antihypertensive agents. Approximately half of the patients received at least 70% of the target indicated care and 42.8% had adequately controlled BP. After adjusting for covariates, patients who received counseling on exercise were positively associated with adequate BP control. Conversely, patients who were prescribed with two or more antihypertensive agents were negatively associated with good BP control.
CONCLUSIONS: These findings indicated that BP control was suboptimal and deficient in the process of care with consequent gaps in guidelines and actual clinical practices. This warrants a re-evaluation of the current strategies and approaches to improve the quality of hypertension management and ultimately to improve outcome.
Method: We used pharmacy dispensing data of 1461 eligible T2DM patients from public primary care clinics in Malaysia treated with oral antidiabetic drugs between January 2018 and May 2019. Adherence rates were calculated during the period preceding the HbA1c measurement. Adherence cut-off values for the following conditions were compared: adherence measure (MPR versus PDC), assessment period (90-day versus 180-day), and HbA1c target (⩽7.0% versus ⩽8.0%).
Results: The optimal adherence cut-offs for MPR and PDC in predicting HbA1c ⩽7.0% ranged between 86.1% and 98.3% across the two assessment periods. In predicting HbA1c ⩽8.0%, the optimal adherence cut-offs ranged from 86.1% to 92.8%. The cut-off value was notably higher with PDC as the adherence measure, shorter assessment period, and a stricter HbA1c target (⩽7.0%) as outcome.
Conclusion: We found that optimal adherence cut-off appeared to be slightly higher than the conventional value of 80%. The adherence thresholds may vary depending on the length of assessment period and outcome definition but a reasonably wise cut-off to distinguish good versus poor medication adherence to be clinically meaningful should be at 90%.
OBJECTIVES: The aim of this study was to cross-culturally adapt and validate the Malay MALMAS (M-MALMAS) in Malaysia.
METHODS: Adults with type 2 diabetes, who could understand Malay, were recruited between May 2016 and February 2017 from a primary care clinic in Kuala Lumpur, Malaysia. The M-MALMAS and the Malay version of the Morisky Medication Adherence Scale (MMAS-8) were administered at baseline to test for convergent validity. Four weeks later, the M-MALMAS was re-administered. Predictive validity of the M-MALMAS was assessed by correlating the medication adherence scores with levels of glycated haemoglobin (HbA1c).
RESULTS: In total, 100 of 104 people agreed to participate (response rate = 96.2%). The overall Cronbach's α and McDonald's Ω for the M-MALMAS was 0.654 and 0.676, respectively (mean = 0.665). At test-retest, no significant difference was found for all items. The median total score interquartile range (IQR) of the M-MALMAS was 7.0 (6.0-8.0) and this was significantly correlated to the median total score of the Malay MMAS-8 [median (IQR) = 7.0 (5.8-8.0), p
METHODS: A cross-sectional study was conducted at two primary care clinics in Kuala Lumpur, Malaysia, recruiting 271 participants by utilizing the universal sampling method. Every patient who attended both the clinics during the study period and met the inclusion and exclusion criteria were approached and briefed about the study. Patients who gave consent were recruited as study participants. Information on sociodemographic, medical condition, and lifestyle behaviors were obtained. The Montreal Cognitive Assessment (MoCA) was used to screen for MCI at a score < 23. The World Health Organization Quality of Life-BREF (WHOQOL-BREF) questionnaire was used to evaluate QOL.
RESULTS: Prevalence of MCI was 27.3%. Lower QOL scores were found in the physical (67.3 ± 1.4), psychological (67.3 ± 1.4), social (66.9 ± 1.6) and environmental (71.3 ± 1.3) domains among participants with MCI. Among them, predictors of QOL were depression in the physical domain, age and stroke in the psychological domain, presence of other types of disorders in the social domain and diabetes and stroke in the environmental domain.
CONCLUSIONS: MCI was prevalent among study participants and were associated with poorer QOL in all domains of QOL. A better understanding of predictors of QOL in older people with MCI is deemed important.
CLINICAL IMPLICATION: Routine cognitive screening at primary care clinics will facilitate early recognition of MCI and facilitates referral to memory clinics for further assessment and treatment.