METHODS: Patients initiating cART between 2006 and 2013 were included. TI was defined as stopping cART for >1 day. Treatment failure was defined as confirmed virological, immunological or clinical failure. Time to treatment failure during cART was analysed using Cox regression, not including periods off treatment. Covariables with P < 0.10 in univariable analyses were included in multivariable analyses, where P < 0.05 was considered statistically significant.
RESULTS: Of 4549 patients from 13 countries in Asia, 3176 (69.8%) were male and the median age was 34 years. A total of 111 (2.4%) had TIs due to AEs and 135 (3.0%) had TIs for other reasons. Median interruption times were 22 days for AE and 148 days for non-AE TIs. In multivariable analyses, interruptions >30 days were associated with failure (31-180 days HR = 2.66, 95%CI (1.70-4.16); 181-365 days HR = 6.22, 95%CI (3.26-11.86); and >365 days HR = 9.10, 95% CI (4.27-19.38), all P < 0.001, compared to 0-14 days). Reasons for previous TI were not statistically significant (P = 0.158).
CONCLUSIONS: Duration of interruptions of more than 30 days was the key factor associated with large increases in subsequent risk of treatment failure. If TI is unavoidable, its duration should be minimised to reduce the risk of failure after treatment resumption.
OBJECTIVE: To investigate the impact of customized CMI (C-CMI) on health-related quality of life (HRQoL) among type 2 diabetes mellitus (T2DM) patients in Qatar.
METHODS: This was a randomized controlled intervention study, in which the intervention group patients received C-CMI and the control group patients received usual care. HRQoL was measured using the EQ-5D-5L questionnaire and EQ visual analog scale (EQ-VAS) at three intervals [i.e. baseline, after 3 months and 6 months].
RESULTS: The EQ-5D-5L index value for the intervention group exhibited sustained improvement from baseline to the third visit. There was a statistically significant difference between groups in the HRQoL utility value (represented as EQ index) at 6 months (0.939 vs. 0.796; p = 0.019). Similarly, the intervention group compared with the control group had significantly greater EQ-VAS at 6 months (90% vs. 80%; p = 0.003).
CONCLUSIONS: The impact of C-CMI on health outcomes of T2DM patients in Qatar reported improvement in HRQoL indicators among the intervention patients. The study built a platform for health policymakers and regulatory agencies to consider the provision of C-CMI in multiple languages.
METHODS: A randomized controlled trial was conducted from December 2014 to April 2015. The home blood pressure monitoring group used an automatic blood pressure device along with standard hypertension outpatient care. Patients were seen at baseline and after 2 months. Medication adherence was measured using a novel validated Medication Adherence Scale (MAS) questionnaire. Office blood pressure and MAS were recorded at both visits. The primary outcomes included evaluation of mean office blood pressure and MAS within groups and between groups at baseline and after 2 months.
RESULTS: Mean changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP) and MAS differed significantly within groups. The home blood pressure monitoring group showed greater mean changes (SBP 17.6 mm Hg, DBP 9.5 mm Hg, MAS 1.5 vs. SBP 14.3 mm Hg, DBP 6.4 mm Hg, MAS 1.3), while between group comparisons showed no significant differences across all variables. The adjusted mean difference for mean SBP was 4.74 (95% confidence interval [CI], -0.65 to 10.13 mm Hg; P=0.084), mean DBP was 1.41 (95% CI, -2.01 to 4.82 mm Hg; P=0.415), and mean MAS was 0.05 (95% CI, -0.29 to 0.40 mm Hg; P=0.768).
CONCLUSION: Short-term home blood pressure monitoring significantly reduced office blood pressure and improved medication adherence, albeit similarly to standard care.
Methods: A multi-center cross sectional study was conducted for a month in out-patient wards of hospitals in Khobar, Dammam, Makkah, and Madinah, Saudi Arabia. Patients were randomly selected from a registered patient pools at hospitals and the item-subject ratio was kept at 1:20. The tool was assessed for factorial, construct, convergent, known group and predictive validities as well as, reliability and internal consistency of scale were also evaluated. Sensitivity, specificity, and accuracy were also evaluated. Data were analyzed using SPSS v24 and MedCalc v19.2. The study was approved by concerned ethics committees (IRB-129-25/6/1439) and (IRB-2019-05-002).
Results: A total of 282 responses were received. The values for normed fit index (NFI), comparative fit index (CFI), Tucker Lewis index (TLI) and incremental fit index (IFI) were 0.960, 0.979, 0.954 and 0.980. All values were >0.95. The value for root mean square error of approximation (RMSEA) was 0.059, i.e., <0.06. Hence, factorial validity was established. The average factor loading of the scale was 0.725, i.e., >0.7, that established convergent validity. Known group validity was established by obtaining significant p-value <0.05, for the associations based on hypotheses. Cronbach's α was 0.865, i.e., >0.7. Predictive validity was established by evaluating odds ratios (OR) of demographic factors with adherence score using logistic regression. Sensitivity was 78.16%, specificity was 76.85% and, accuracy of the tool was 77.66%, i.e., >70%.
Conclusion: The Arabic version of GMAS achieved all required statistical parameters and was validated in Saudi patients with chronic diseases.
Purpose: To determine the level of adherence to opioid analgesics in patients with cancer pain and to identify factors that may influence the adherence.
Patient and Methods: This was a cross-sectional study conducted from March to June 2018 at two tertiary care hospitals in Malaysia. Study instruments consisted of a set of validated questionnaires; the Medication Compliance Questionnaire, Brief Pain Inventory and Pain Opioid Analgesic Beliefs─Cancer scale.
Results: A total of 134 patients participated in this study. The patients' adherence scores ranged from 52-100%. Factors with a moderate, statistically significant negative correlation with adherence were negative effect beliefs (rs= -0.53, p<0.001), pain endurance beliefs (rs = -0.49, p<0.001) and the use of aqueous morphine (rs = -0.26, p=0.002). A multiple linear regression model on these predictors resulted in a final model which accounted for 47.0% of the total variance in adherence (R2 = 0.47, F (7, 126) = 15.75, p<0.001). After controlling for other variables, negative effect beliefs were the strongest contributor to the model (β = -0.39, p<0.001) and uniquely explained 12.3% of the total variance.
Conclusion: The overall adherence to opioid analgesics among Malaysian patients with cancer pain was good. Negative effects beliefs regarding cancer pain and opioids strongly predicted adherence.
METHODS: This prospective study was conducted among the caregivers of 443 child TB patients registered during the study. Caregivers of children were queried using a structured questionnaire consisting of sociodemographic and socio-economic factors and the role of healthcare workers during the treatment course. Risk factors for non-adherence were estimated using a logistic regression model.
RESULTS: In multivariate analysis, the independent variables that had a statistically significant positive association with non-adherence were male sex (adjusted odds ratio [AOR] 5.870 [95% confidence interval {CI} 1.99 to 17.29]), age ≥45 y (AOR 5.627 [95% CI 1.88 to 16.82]), caregivers with no formal education (AOR 3.905 [95% CI 1.29 to 11.79]), financial barriers (AOR 30.297 [95% CI 6.13 to 149.54]), insufficient counselling by healthcare workers (AOR 5.319 [95% CI 1.62 to 17.42]), insufficient counselling by health professionals (AOR 4.117 [95% CI 1.05 to 16.05]) and unfriendly attitude and poor support from healthcare professionals (AOR 11.150 [95% CI 1.91 to 65.10]).
CONCLUSIONS: Treatment adherence in the present study was 86% using the Morisky Green Levine Medication Adherence Scale and 90.7% using the visual analogue scale tool. Predictors of non-adherence need to be a focus and caregivers should be given complete knowledge about the importance of adherence to TB treatment.
Objective: To investigate medication adherence among patients with and without medication subsidies and to identify factors that may influence patients' adherence to medication. Setting: Government healthcare institutions in Kuala Lumpur, Selangor, and Negeri Sembilan and private healthcare institutions in Selangor and Negeri Sembilan, Malaysia.
Methods: This cross-sectional study sampled patients with and without medication subsidies (self-paying patients). Only one of the patient's medications was re-packed into Medication Event Monitoring Systems (MEMS) bottles, which were returned after four weeks. Adherence was defined as the dose regimen being executed as prescribed on 80% or more of the days. The factors that may influence patients' adherence were modelled using binary logistic regression. Main outcome measure: Percentage of medication adherence.
Results: A total of 97 patients, 50 subsidized and 47 self-paying, were included in the study. Medication adherence was observed in 50% of the subsidized patients and 63.8% of the self-paying patients (χ2=1.887, df=1, p=0.219). None of the evaluated variables had a significant influence on patients' medication adherence, with the exception of attending drug counselling. Patients who attended drug counselling were found to be 3.3 times more likely to adhere to medication than those who did not (adjusted odds ratio of 3.29, 95% CI was 1.42 to 7.62, p = 0.006).
Conclusion: There is no significant difference in terms of medication adherence between subsidized and self-paying patients. Future studies may wish to consider evaluating modifiable risk factors in the examination of non-adherence among subsidized and self-paying patients in Malaysia.