RESEARCH DESIGN AND METHODS: The prevalence of diabetes, defined as self-reported or fasting glycemia ≥7 mmol/L, was documented in 119,666 adults from three high-income (HIC), seven upper-middle-income (UMIC), four lower-middle-income (LMIC), and four low-income (LIC) countries. Relationships between diabetes and its risk factors within these country groupings were assessed using multivariable analyses.
RESULTS: Age- and sex-adjusted diabetes prevalences were highest in the poorer countries and lowest in the wealthiest countries (LIC 12.3%, UMIC 11.1%, LMIC 8.7%, and HIC 6.6%; P < 0.0001). In the overall population, diabetes risk was higher with a 5-year increase in age (odds ratio 1.29 [95% CI 1.28-1.31]), male sex (1.19 [1.13-1.25]), urban residency (1.24 [1.11-1.38]), low versus high education level (1.10 [1.02-1.19]), low versus high physical activity (1.28 [1.20-1.38]), family history of diabetes (3.15 [3.00-3.31]), higher waist-to-hip ratio (highest vs. lowest quartile; 3.63 [3.33-3.96]), and BMI (≥35 vs. <25 kg/m(2); 2.76 [2.52-3.03]). The relationship between diabetes prevalence and both BMI and family history of diabetes differed in higher- versus lower-income country groups (P for interaction < 0.0001). After adjustment for all risk factors and ethnicity, diabetes prevalences continued to show a gradient (LIC 14.0%, LMIC 10.1%, UMIC 10.9%, and HIC 5.6%).
CONCLUSIONS: Conventional risk factors do not fully account for the higher prevalence of diabetes in LIC countries. These findings suggest that other factors are responsible for the higher prevalence of diabetes in LIC countries.
RESEARCH DESIGN AND METHODS: The Prospective Urban Rural Epidemiology (PURE) study enrolled 143,567 adults aged 35-70 years from 4 high-income countries (HIC), 12 middle-income countries (MIC), and 5 low-income countries (LIC). The mean follow-up was 9.0 ± 3.0 years.
RESULTS: Among those with diabetes, CVD rates (LIC 10.3, MIC 9.2, HIC 8.3 per 1,000 person-years, P < 0.001), all-cause mortality (LIC 13.8, MIC 7.2, HIC 4.2 per 1,000 person-years, P < 0.001), and CV mortality (LIC 5.7, MIC 2.2, HIC 1.0 per 1,000 person-years, P < 0.001) were considerably higher in LIC compared with MIC and HIC. Within LIC, mortality was higher in those in the lowest tertile of wealth index (low 14.7%, middle 10.8%, and high 6.5%). In contrast to HIC and MIC, the increased CV mortality in those with diabetes in LIC remained unchanged even after adjustment for behavioral risk factors and treatments (hazard ratio [95% CI] 1.89 [1.58-2.27] to 1.78 [1.36-2.34]).
CONCLUSIONS: CVD rates, all-cause mortality, and CV mortality were markedly higher among those with diabetes in LIC compared with MIC and HIC with mortality risk remaining unchanged even after adjustment for risk factors and treatments. There is an urgent need to improve access to care to those with diabetes in LIC to reduce the excess mortality rates, particularly among those in the poorer strata of society.
METHODS: Through the Association of Southeast Asian Nations Costs in Oncology study, 1490 newly diagnosed cancer patients were followed-up in Malaysia for 1 year. Health-related quality of life was assessed by using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and EuroQol-5 (EQ-5D) dimension questionnaires at baseline, 3 and 12 months. Psychological distress was assessed by using Hospital Anxiety and Depression Scale. Data were modeled by using general linear and logistic regressions analyses.
RESULTS: One year after diagnosis, the mean EORTC QLQ-C30 Global Health score of the cancer survivors remained low at 53.0 over 100 (SD 21.4). Fifty-four percent of survivors reported at least moderate levels of anxiety, while 27% had at least moderate levels of depression. Late stage at diagnosis was the strongest predictor of low HRQoL. Increasing age, being married, high-income status, hospital type, presence of comorbidities, and chemotherapy administration were also associated with worse HRQoL. The significant predictors of psychological distress were cancer stage and hospital type.
CONCLUSION: Cancer survivors in this middle-income setting have persistently impaired HRQoL and high levels of psychological distress. Development of a holistic cancer survivorship program addressing wider aspects of well-being is urgently needed in our settings.
DESCRIPTION: COVID-19 directly affects pregnant women causing more severe disease and adverse pregnancy outcomes. The indirect effects due to the monumental COVID-19 response are much worse, increasing maternal and neonatal mortality.
ASSESSMENT: Amidst COVID-19, governments must balance effective COVID-19 response measures while continuing delivery of essential health services. Using the World Health Organization's operational guidelines as a base, countries must conduct contextualized analyses to tailor their operations. Evidence based information on different services and comparative cost-benefits will help decisions on trade-offs. Situational analyses identifying extent and reasons for service disruptions and estimates of impacts using modelling techniques will guide prioritization of services. Ensuring adequate supplies, maintaining core interventions, expanding non-physician workforce and deploying telehealth are some adaptive measures to optimize care. Beyond the COVID-19 pandemic, governments must reinvest in maternal and child health by building more resilient maternal health services supported by political commitment and multisectoral engagement, and with assistance from international partners.
CONCLUSIONS: Multi-sectoral investments providing high-quality care that ensures continuity and available to all segments of the population are needed. A robust primary healthcare system linked to specialist care and accessible to all segments of the population including marginalized subgroups is of paramount importance. Systematic approaches to digital health care solutions to bridge gaps in service is imperative. Future pandemic preparedness programs must include action plans for resilient maternal health services.
Objective: To identify any associations between depressive symptoms and incident CVD and all-cause mortality in countries at different levels of economic development and in urban and rural areas.
Design, Setting, and Participants: This multicenter, population-based cohort study was conducted between January 2005 and June 2019 (median follow-up, 9.3 years) and included 370 urban and 314 rural communities from 21 economically diverse countries on 5 continents. Eligible participants aged 35 to 70 years were enrolled. Analysis began February 2018 and ended September 2019.
Exposures: Four or more self-reported depressive symptoms from the Short-Form Composite International Diagnostic Interview.
Main Outcomes and Measures: Incident CVD, all-cause mortality, and a combined measure of either incident CVD or all-cause mortality.
Results: Of 145 862 participants, 61 235 (58%) were male and the mean (SD) age was 50.05 (9.7) years. Of those, 15 983 (11%) reported 4 or more depressive symptoms at baseline. Depression was associated with incident CVD (hazard ratio [HR], 1.14; 95% CI, 1.05-1.24), all-cause mortality (HR, 1.17; 95% CI, 1.11-1.25), the combined CVD/mortality outcome (HR, 1.18; 95% CI, 1.11-1.24), myocardial infarction (HR, 1.23; 95% CI, 1.10-1.37), and noncardiovascular death (HR, 1.21; 95% CI, 1.13-1.31) in multivariable models. The risk of the combined outcome increased progressively with number of symptoms, being highest in those with 7 symptoms (HR, 1.24; 95% CI, 1.12-1.37) and lowest with 1 symptom (HR, 1.05; 95% CI, 0.92 -1.19; P for trend
METHODS: Data on 123 obese and overweight housewives in the intervention group from the MyBFF@home study were utilised. A validated Malaysian Malay version of Obesity Weight Loss Quality of Life (OWLQOL) questionnaire was administered at baseline and 6 months after intervention. Descriptive analysis, univariate analysis, paired t-test and multiple logistic regression were performed using SPSS Version 22.
RESULTS: Mean body mass index (BMI) was 31.5 kg/m2 (SD:4.13), with 51 participants classified as overweight (41.5%) while 72 were obese (58.5%). About 72% of the housewives experienced weight reduction (62% reduced weight less than 5% and 11% reduced weight more than 5% of their baseline weight). There was a significant improvement in HRQOL with a pre-intervention total mean score of 59.82 (SD: 26.60) and post-intervention of 66.13 (SD: 22.82), p-value