DATA SOURCES: The articles published in PubMed, MEDLINE and Google Scholar were searched using text words: off-label, unlicensed, paediatric and children. Additional articles were identified by reviewing the bibliography of the retrieved articles. Full-text articles published in English which reported on the prevalence of off-label prescribing in children between January 1996 and December 2016 were included.
RESULTS: A total of 101 studies met the inclusion criteria. Off-label prescribing definition included four main categories: age, indication, dose and route of administration. The three most common reference sources used in the studies were summary of product characteristics, national formularies and package inserts. Overall, the off-label prescribing rates in children ranged from 1.2 to 99.7%. The most common category of off-label prescribing in children was dose and age.
CONCLUSIONS: This review highlighted that off-label prescribing in children was found to be highly prevalent throughout the past two decades, persistently in the neonatal intensive care units. This suggests that besides legislative and regulatory initiatives, behavioural, knowledge aspects and efforts to integrate evidence into practice related to off-label prescribing also need to be evaluated and consolidated as part of the concerted efforts to narrow the gaps in prescribing for children.
AIM: To assess the diabetes empowerment scores and its correlated factors among type 2 diabetes patients in a primary care clinic in Malaysia.
METHODS: This is a cross sectional study involving 322 patients with type 2 diabetes mellitus (DM) followed up in a primary care clinic. Systematic sampling method was used for patient recruitment. The Diabetes Empowerment Scale (DES) questionnaire was used to measure patient empowerment. It consists of three domains: (1) Managing the psychosocial aspect of diabetes (9 items); (2) Assessing dissatisfaction and readiness to change (9 items); and (3) Setting and achieving diabetes goal (10 items). A score was considered high if it ranged from 100 to 140. Data analysis was performed using SPSS version 25 and multiple linear regressions was used to identify the predictors of total diabetes empowerment scores.
RESULTS: The median age of the study population was 55 years old. 56% were male and the mean duration of diabetes was 4 years. The total median score of the DES was 110 [interquartile range (IQR) = 10]. The median scores of the three subscales were 40 with (IQR = 4) for "Managing the psychosocial aspect of diabetes"; 36 with (IQR = 3) for "Assessing dissatisfaction and readiness to change"; and 34 with (IQR = 5) for "Setting and achieving diabetes goal". According to multiple linear regressions, factors that had significant correlation with higher empowerment scores among type 2 diabetes patients included an above secondary education level (P < 0.001), diabetes education exposure (P = 0.003), lack of ischemic heart disease (P = 0.017), and lower glycated hemoglobin (HbA1c) levels (P < 0.001).
CONCLUSION: Diabetes empowerment scores were high among type 2 diabetes patients in this study population. Predictors for high empowerment scores included above secondary education level, diabetes education exposure, lack of ischemic heart disease status and lower HbA1c.
METHOD: Baseline data from a large study entitled Evaluation of Enhanced Primary Health Care interventions in public health clinics (EnPHC-EVA: Facility) were used in this analysis. Data from 40 public primary care clinics were collected through retrospective chart reviews and a patient exit survey. We calculated the ICCs for processes of care, clinical outcomes and patient experiences in patients with T2D and/or hypertension using the analysis of variance approach.
RESULTS: Patient experience had the highest ICC values compared to processes of care and clinical outcomes. The ICC values ranged from 0.01 to 0.48 for processes of care. Generally, the ICC values for processes of care for patients with hypertension only are higher than those for T2D patients, with or without hypertension. However, both groups of patients have similar ICCs for antihypertensive medications use. In addition, similar ICC values were observed for clinical outcomes, ranging from 0.01 to 0.09. For patient experience, the ICCs were between 0.03 (proportion of patients who are willing to recommend the clinic to their friends and family) and 0.25 (for Patient Assessment of Chronic Illness Care item 9, Given a copy of my treatment plan).
CONCLUSION: The reported ICCs and their respective 95% confidence intervals for T2D and hypertension will be useful for estimating sample sizes and improving efficiency of cluster trials conducted in the primary care setting, particularly for low- and middle-income countries.
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%.
DESIGN: Cross-sectional study.
SETTING: Three public primary care clinics in a district in Selangor, Malaysia.
PARTICIPANTS: Registered patients aged 55 years and above.
MEASUREMENTS: A face-to-face interview was conducted using a validated questionnaire of Medical Outcome Study 36-item short form health survey (SF-36). The outcome measure was the health related quality of life (HRQoL) and other factors measured were socio demography, physical activity, social support (Duke-UNC Functional Social Support Questionnaire), and presence of non-communicable diseases.
RESULTS: A total of 347 participants had non-communicable diseases which included hypertension (41.8%), type 2 diabetes (33.7%), asthma (4.8%), hyperlipidaemia (1.7%), coronary heart disease (1.2%), and osteoarthritis (0.2%). Age ≥ 65 years old (OR =2.23; 95%CI=1.42, 3.50), single (OR=1.75; 95%CI=1.06,2.90), presence of co-morbid condition (OR=1.66; 95%CI=1.06, 2.61), and poorer social support (OR=2.11; 95%CI=1.27, 3.51; p=0.002) were significant predictors of poorer physical component of HRQoL . In predicting lower mental health component of HRQoL, the significant predictors were women (OR=2.28; 95%CI=1.44, 3.62), Indian ethnicity (OR=1.86; 95%CI=1.08, 3.21) and poorer social support (OR=2.71; 95%CI=1.63, 4.51). No interactions existed between these predictors.
CONCLUSION: Older people with non-communicable diseases were susceptible to lower health related quality of life. Increasing age, single, presence of co-morbid conditions, and poorer social support were predictors of lower physical health component of HRQoL. While the older women, Indian ethnicity and poorer social support reported lower mental health component of HRQoL.