METHODS: Between 2009 and 2012, a kilometre-long walk was completed by trained investigators in 462 communities across 16 countries to collect data on tobacco marketing. We interviewed community members about their exposure to traditional and non-traditional marketing in the previous six months. To examine differences in marketing between urban and rural communities and between high-, middle- and low-income countries, we used multilevel regression models controlling for potential confounders.
FINDINGS: Compared with high-income countries, the number of tobacco advertisements observed was 81 times higher in low-income countries (incidence rate ratio, IRR: 80.98; 95% confidence interval, CI: 4.15-1578.42) and the number of tobacco outlets was 2.5 times higher in both low- and lower-middle-income countries (IRR: 2.58; 95% CI: 1.17-5.67 and IRR: 2.52; CI: 1.23-5.17, respectively). Of the 11,842 interviewees, 1184 (10%) reported seeing at least five types of tobacco marketing. Self-reported exposure to at least one type of traditional marketing was 10 times higher in low-income countries than in high-income countries (odds ratio, OR: 9.77; 95% CI: 1.24-76.77). For almost all measures, marketing exposure was significantly lower in the rural communities than in the urban communities.
CONCLUSION: Despite global legislation to limit tobacco marketing, it appears ubiquitous. The frequency and type of tobacco marketing varies on the national level by income group and by community type, appearing to be greatest in low-income countries and urban communities.
DESIGN: Using data from the 2011-2012 US National Health and Nutrition Examination Surveys, we examined the relationship between HbA1c and a single fasting measure of blood glucose in a non-clinical population of people with known diabetes (n=333). A linear equation for estimating HbA1c from blood glucose was developed. Appropriate blood glucose cut-off values were set for poor glycaemic control (HbA1c≥69.4 mmol/mol).
RESULTS: The HbA1c and blood glucose measures were well correlated (r=0.7). Three blood glucose cut-off values were considered for classifying poor glycaemic control: 8.0, 8.9, and 11.4 mmol/L. A blood glucose of 11.4 had a specificity of 1, but poor sensitivity (0.37); 8.9 had high specificity (0.94) and moderate sensitivity (0.7); 8.0 was associated with good specificity (0.81) and sensitivity (0.75).
CONCLUSIONS: Where HbA1c measurement is too expensive for community surveillance, a single blood glucose measure may be a reasonable alternative. Generalising the specific results from these US data to low resource settings may not be appropriate, but the general approach is worthy of further investigation.
METHODS AND ANALYSIS: This is a community-based feasibility study focused on the assessment of cognition, embedded in the longitudinal study of health and demographic surveillance site of the South East Asia Community Observatory (SEACO), in Malaysia. In total, 200 adults aged ≥50 years are selected for an in-depth health and cognitive assessment including the Mini Mental State Examination, the Montreal Cognitive Assessment, blood pressure, anthropometry, gait speed, hand grip strength, Depression Anxiety Stress Score and dried blood spots.
DISCUSSION AND CONCLUSIONS: The results will inform the feasibility, response rates and operational challenges for establishing an ageing study focused on cognitive function in similar middle-income country settings. Knowing the burden of cognitive impairment and dementia and risk factors for disease will inform local health priorities and management, and place these within the context of increasing life expectancy.
ETHICS AND DISSEMINATION: The study protocol is approved by the Monash University Human Research Ethics Committee. Informed consent is obtained from all the participants. The project's analysed data and findings will be made available through publications and conference presentations and a data sharing archive. Reports on key findings will be made available as community briefs on the SEACO website.
METHODS: A total of 1844 (780 males and 1064 females) known diabetics aged ≥ 35 years were identified from the South East Asia Community Observatory (SEACO) health and demographic surveillance site database.
RESULTS: 41.3% of the sample had poor glycaemic control. Poor glycaemic control was associated with age and ethnicity, with older participants (65+) better controlled than younger adults (45-54), and Malaysian Indians most poorly controlled, followed by Malay and then Chinese participants. Metabolic risk factors were also highly associated with poor glycaemic control.
CONCLUSIONS: There is a critical need for evidence for a better understanding of the mechanisms of the associations between risk factors and glycaemic control.
SETTING: The study was conducted in the capital city of Thiruvananthapuram in the South Indian state of Kerala.
PARTICIPANTS: The study participants were community-dwelling individuals aged 60 years and above.
PRIMARY OUTCOME MEASURE: MCI was the primary outcome measure and was defined using the criteria by European Alzheimer's Disease Consortium. Cognitive assessment was done using the Malayalam version of Addenbrooke's Cognitive Examination tool. Data were also collected on sociodemographic variables, self-reported comorbidities like hypertension and diabetes, lifestyle factors, depression, anxiety and activities of daily living.
RESULTS: The prevalence of MCI was found to be 26.06% (95% CI of 22.12 to 30.43). History of imbalance on walking (adjusted OR 2.75; 95 % CI of 1.46 to 5.17), presence of depression (adjusted OR 2.17, 95 % CI of 1.21 to 3.89), anxiety (adjusted OR 2.22; 95 % CI of 1.21 to 4.05) and alcohol use (adjusted OR 1.99; 95 % CI of 1.02 to 3.86) were positively associated with MCI while leisure activities at home (adjusted OR 0.33; 95 % CI of 0.11 to 0.95) were negatively associated.
CONCLUSION: The prevalence of MCI is high in Kerala. It is important that the health system and the government take up urgent measures to tackle this emerging public health issue.
OBJECTIVE: This study aims to examine and contrast the perceptions of MS patients, neurologists, and palliative care physicians towards providing palliative care for patients with MS in Malaysia.
METHODS: 12 MS patients, 5 neurologists, and 5 palliative care physicians participated in this qualitative study. Each participant took part in a semi-structured interview. The interviews were transcribed verbatim, and analysed using an iterative thematic analysis approach.
RESULTS: Patients and neurologists mostly associated palliative care with the end-of-life and struggled to understand the need for palliative care in MS. Another barrier was the lack of understanding about the palliative care needs of MS patients. Palliative care physicians also identified the scarcity of resources and their lack of experience with MS as barriers. The current referral-based care pathway itself was found to be a barrier to the provision of palliative care.
CONCLUSIONS: MS patients in Malaysia face several barriers in accessing palliative care. Overcoming these barriers will require improving the shared understanding of palliative care and its role in MS. The existing care pathway also needs to be reformed to ensure that it improves access to palliative care for MS patients.
OBJECTIVES: This study aimed to collect real-world cost and HRQOL data, and investigate their associations with multiple disease-severity indicators among AD patients in Thailand.
METHODS: We recruited AD patients aged ≥60 years accompanied by their caregivers at a university-affiliated tertiary hospital. A one-time structured interview was conducted to collect disease-severity indicators, HRQOL, and caregiving information using standardized tools. The hospital's database was used to retrieve healthcare resource utilization occurred over 6 months preceding the interview date. Costs were annualized and stratified based on cognitive status. Generalized linear models were employed to evaluate determinants of costs and HRQOL.
RESULTS: Among 148 community-dwelling patients, average annual total societal costs of AD care were $8014 (95% confidence interval [CI]: $7295-$8844) per patient. Total costs of patients with severe stage ($9860; 95% CI: $8785-$11 328) were almost twice as high as those of mild stage ($5524; 95% CI: $4649-$6593). The major cost driver was direct medical costs, particularly those incurred by AD prescriptions. Functional status was the strongest determinant for both total costs and patient's HRQOL (P value
METHODS: Data were from the 10/66 Study. Individuals aged 65 years or older and without dementia at baseline were selected from China, Cuba, the Dominican Republic, Mexico, Peru, Puerto Rico, and Venezuela. Dementia incidence was assessed over 3-5 years, with diagnosis according to the 10/66 Study diagnostic algorithm. Discrimination and calibration were tested for five models: the Cardiovascular Risk Factors, Aging and Dementia risk score (CAIDE); the Study on Aging, Cognition and Dementia (AgeCoDe) model; the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI); the Brief Dementia Screening Indicator (BDSI); and the Rotterdam Study Basic Dementia Risk Model (BDRM). Models were tested with use of Cox regression. The discriminative accuracy of each model was assessed using Harrell's concordance (c)-statistic, with a value of 0·70 or higher considered to indicate acceptable discriminative ability. Calibration (model fit) was assessed statistically using the Grønnesby and Borgan test.
FINDINGS: 11 143 individuals without baseline dementia and with available follow-up data were included in the analysis. During follow-up (mean 3·8 years [SD 1·3]), 1069 people progressed to dementia across all sites (incidence rate 24·9 cases per 1000 person-years). Performance of the models varied. Across countries, the discriminative ability of the CAIDE (0·52≤c≤0·63) and AgeCoDe (0·57≤c≤0·74) models was poor. By contrast, the ANU-ADRI (0·66≤c≤0·78), BDSI (0·62≤c≤0·78), and BDRM (0·66≤c≤0·78) models showed similar levels of discriminative ability to those of the development cohorts. All models showed good calibration, especially at low and intermediate levels of predicted risk. The models validated best in Peru and poorest in the Dominican Republic and China.
INTERPRETATION: Not all dementia prediction models developed in HICs can be simply extrapolated to LMICs. Further work defining what number and which combination of risk variables works best for predicting risk of dementia in LMICs is needed. However, models that transport well could be used immediately for dementia prevention research and targeted risk reduction in LMICs.
FUNDING: National Institute for Health Research, Wellcome Trust, WHO, US Alzheimer's Association, and European Research Council.
METHODS AND ANALYSIS: Primary outcomes focus on feasibility measures of recruitment, retention, implementation and acceptability of the intervention. Secondary outcomes will include blood pressure, cognitive function, body composition and physical function (including muscle strength and gait speed). Adherence to the dietary intervention will be assessed through collection of biological samples, 24-hour recall and Food Frequency Questionnaire. A subgroup of participants will also complete postintervention focus groups to further explore the feasibility considerations of executing a larger trial, the ability of these individuals to make dietary changes and the barriers and facilitators associated with implementing these changes.
ETHICS AND DISSEMINATION: Ethical approval has been obtained from Monash University Human Research Ethics Committee and Medical Research and Ethics Committee of Malaysia. Results of the study will be disseminated via peer-reviewed publications and presentations at national and international conferences.ISRCTN47562685; Pre-results.