OBJECTIVES: The GBD (Global Burden of Disease) 2015 study integrated data on disease incidence, prevalence, and mortality to produce consistent, up-to-date estimates for cardiovascular burden.
METHODS: CVD mortality was estimated from vital registration and verbal autopsy data. CVD prevalence was estimated using modeling software and data from health surveys, prospective cohorts, health system administrative data, and registries. Years lived with disability (YLD) were estimated by multiplying prevalence by disability weights. Years of life lost (YLL) were estimated by multiplying age-specific CVD deaths by a reference life expectancy. A sociodemographic index (SDI) was created for each location based on income per capita, educational attainment, and fertility.
RESULTS: In 2015, there were an estimated 422.7 million cases of CVD (95% uncertainty interval: 415.53 to 427.87 million cases) and 17.92 million CVD deaths (95% uncertainty interval: 17.59 to 18.28 million CVD deaths). Declines in the age-standardized CVD death rate occurred between 1990 and 2015 in all high-income and some middle-income countries. Ischemic heart disease was the leading cause of CVD health lost globally, as well as in each world region, followed by stroke. As SDI increased beyond 0.25, the highest CVD mortality shifted from women to men. CVD mortality decreased sharply for both sexes in countries with an SDI >0.75.
CONCLUSIONS: CVDs remain a major cause of health loss for all regions of the world. Sociodemographic change over the past 25 years has been associated with dramatic declines in CVD in regions with very high SDI, but only a gradual decrease or no change in most regions. Future updates of the GBD study can be used to guide policymakers who are focused on reducing the overall burden of noncommunicable disease and achieving specific global health targets for CVD.
MATERIAL AND METHODS: This was a five-year retrospective open cohort study using secondary data from the National Diabetes Registry. The study setting was all public health clinics (n = 47) in the state of Negeri Sembilan, Malaysia. Time to treatment intensification was defined as the number of years from the index year until the addition of another oral antidiabetic drug or initiation of insulin. Life table survival analysis based on best-worst case scenarios was used to determine the time to treatment intensification. Discrete-time proportional hazards model was fitted for the factors associated with treatment intensification.
RESULTS: The mean follow-up duration was 2.6 (SD 1.1) years. Of 7,646 patients, the median time to treatment intensification was 1.29 years (15.5 months), 1.58 years (19.0 months) and 2.32 years (27.8 months) under the best-, average- and worst-case scenarios respectively. The proportion of patients with treatment intensification was 45.4% (95% CI: 44.2-46.5), of which 34.6% occurred only after one year. Younger adults, overweight, obesity, use of antiplatelet medications and poorer HbA1c were positively associated with treatment intensification. Patients treated with more oral antidiabetics were less likely to have treatment intensification.
CONCLUSION: Clinical inertia is present in the management of T2D patients in Malaysian public health clinics. We recommend further studies in lower- and middle-income countries to explore its causes so that targeted strategies can be developed to address this issue.
METHODS AND FINDINGS: The web-based Joint Asia Diabetes Evaluation (JADE) platform provides a protocol to guide data collection for issuing a personalized JADE report including risk categories (1-4, low-high), 5-year probabilities of cardiovascular-renal events, and trends and targets of 4 risk factors with tailored decision support. The JADE program is a prospective cohort study implemented in a naturalistic environment where patients underwent nurse-led structured evaluation (blood/urine/eye/feet) in public and private outpatient clinics and diabetes centers in Hong Kong. We retrospectively analyzed the data of 16,624 Han Chinese patients with type 2 diabetes who were enrolled in 2007-2015. In the public setting, the non-JADE group (n = 3,587) underwent structured evaluation for risk factors and complications only, while the JADE (n = 9,601) group received a JADE report with group empowerment by nurses. In a community-based, nurse-led, university-affiliated diabetes center (UDC), the JADE-Personalized (JADE-P) group (n = 3,436) received a JADE report, personalized empowerment, and annual telephone reminder for reevaluation and engagement. The primary composite outcome was time to the first occurrence of cardiovascular-renal diseases, all-site cancer, and/or death, based on hospitalization data censored on 30 June 2017. During 94,311 person-years of follow-up in 2007-2017, 7,779 primary events occurred. Compared with the JADE group (136.22 cases per 1,000 patient-years [95% CI 132.35-140.18]), the non-JADE group had higher (145.32 [95% CI 138.68-152.20]; P = 0.020) while the JADE-P group had lower event rates (70.94 [95% CI 67.12-74.91]; P < 0.001). The adjusted hazard ratios (aHRs) for the primary composite outcome were 1.22 (95% CI 1.15-1.30) and 0.70 (95% CI 0.66-0.75), respectively, independent of risk profiles, education levels, drug usage, self-care, and comorbidities at baseline. We reported consistent results in propensity-score-matched analyses and after accounting for loss to follow-up. Potential limitations include its nonrandomized design that precludes causal inference, residual confounding, and participation bias.
CONCLUSIONS: ICT-assisted integrated care was associated with a reduction in clinical events, including death in type 2 diabetes in public and private healthcare settings.
METHODS: A retrospective cohort analysis of patients aged ≥12 years, diagnosed with an ALRTI in primary care in 2014-15 was conducted using data from the Clinical Practice Research Datalink. Current asthma status, asthma medication and oral antibiotic use within 3 days of ALRTI infection was determined. Treatment frequency was calculated by asthma status. Mixed-effect regression models were used to explore between-practice variation and treatment determinants.
RESULTS: There were 127,976 ALRTIs reported among 110,418 patients during the study period, of whom 17,952 (16%) had asthma. Respectively, 81 and 79% of patients with and without asthma received antibiotics, and 41 and 15% asthma medication. There were significant differences in between-practice prescribing for all treatments, with greatest differences seen for oral steroids (odds ratio (OR) 18; 95% CI 7-82 and OR = 94; 33-363, with and without asthma) and asthma medication only (OR 7; 4-18 and OR = 17; 10-33, with and without asthma). Independent predictors of antibiotic prescribing among patients with asthma included fewer previous ALRTI presentations (≥2 vs. 0 previous ALRTI: OR = 0.25; 0.16-0.39), higher practice (OR = 1.47; 1.35-1.60 per SD) and prior antibiotic prescribing (3+ vs. 1 prescriptions OR = 1.28; 1.04-1.57) and concurrent asthma medication (OR = 1.44; 1.32-1.57). Independent predictors of asthma medication in patients without asthma included higher prior asthma medication prescribing (≥7 vs. 0 prescriptions OR = 2.31; 1.83-2.91) and concurrent antibiotic prescribing (OR = 3.59; 3.22-4.01).
CONCLUSION: Findings from the study indicate that antibiotics are over-used for ALRTI, irrespective of asthma status, and asthma medication is over-used in patients without asthma, with between-practice variation suggesting considerable clinical uncertainty. Further research is urgently needed to clarify the role of these medications for ALRTI.