METHODS: Cytochrome b gene sequences (479 bp) generated from India and available at MalAvi database were used to study the avian haemosporidian prevalence and phylogenetic analysis of lineages at local and world levels.
RESULTS: One common (COLL2) and only once in the study (CYOPOL01, CHD01, CYORUB01, EUMTHA01, GEOCIT01) haemosporidian lineages were discovered. 5.88% prevalence of haemosporidian infection was found in 102 samples belonging to 6 host species. Haemoproteus prevalence was 4.90% across five host species (Phylloscopus trochiloides, Cyornis poliogenys, C. hainanus dialilaemus, C. rubeculoides, Eumiyas thalassinus) and Plasmodium prevalence was 0.98% in Geokichla citrina. Spatial phylogeny at the global level showed that COLL2 lineage, found in C. poliogenys in India, was genetically identical to H. pallidus lineages (COLL2) in parts of Africa, Europe, North America, Malaysia, and the Philippines. The Plasmodium lineage (GEOCIT01) was related to PADOM16 in Egypt, but the sequences were only 93.89% alike.
CONCLUSIONS: Four new lineages of Haemoproteus and one of Plasmodium were reported. COLL2 similarity with other H. pallidus lineages may suggest their hosts as possible infection sources.
DESIGN: Population-based cross-sectional study.
SETTING: South East Asia Community Observatory HDSS site in Malaysia.
PARTICIPANTS: Of 45 246 participants recruited from 13 431 households, 18 101 eligible adults aged 18-97 years (mean age 47 years, 55.6% female) were included.
MAIN OUTCOME MEASURES: The main outcome was prevalence of multimorbidity. Multimorbidity was defined as the coexistence of two or more chronic conditions per individual. A total of 13 chronic diseases were selected and were further classified into 11 medical conditions to account for multimorbidity. The conditions were heart disease, stroke, diabetes mellitus, hypertension, chronic kidney disease, musculoskeletal disorder, obesity, asthma, vision problem, hearing problem and physical mobility problem. Risk factors for multimorbidity were also analysed.
RESULTS: Of the study cohort, 28.5% people lived with multimorbidity. The individual prevalence of the chronic conditions ranged from 1.0% to 24.7%, with musculoskeletal disorder (24.7%), obesity (20.7%) and hypertension (18.4%) as the most prevalent chronic conditions. The number of chronic conditions increased linearly with age (p<0.001). In the logistic regression model, multimorbidity is associated with female sex (adjusted OR 1.28, 95% CI 1.17 to 1.40, p<0.001), education levels (primary education compared with no education: adjusted OR 0.63, 95% CI 0.53 to 0.74; secondary education: adjusted OR 0.60, 95% CI 0.51 to 0.70; tertiary education: adjusted OR 0.65, 95% CI 0.54 to 0.80; p<0.001) and employment status (working adults compared with retirees: adjusted OR 0.70, 95% CI 0.60 to 0.82, p<0.001), in addition to age (adjusted OR 1.05, 95% CI 1.05 to 1.05, p<0.001).
CONCLUSIONS: The current single-disease services in primary and secondary care should be accompanied by strategies to address complexities associated with multimorbidity, taking into account the factors associated with multimorbidity identified. Future research is needed to identify the most commonly occurring clusters of chronic diseases and their risk factors to develop more efficient and effective multimorbidity prevention and treatment strategies.
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: Studies assessing sodium intake in adults aged 18 years and above with reported elevated blood pressure will be included. Five electronic databases (MEDLINE, Embase, Global Health, WoS and Cochrane CENTRAL) will be systematically searched from inception to March 2021. Also, a manual search of bibliographies and grey literature will be conducted. Two reviewers will screen the records independently for eligibility. One reviewer will extract all data, and two others will review the extracted data for accuracy. The methodological quality of included studies will be evaluated based on three scoring systems: (1) National Heart, Lung and Blood Institute for interventional studies; (2) Biomarker-based Cross-sectional Studies for biomarker-based observational studies and (3) European Micronutrient Recommendation Aligned Network of Excellence for validation studies of dietary self-report instruments.
ETHICS AND DISSEMINATION: As the proposed systematic review will collect and analyse secondary data associated with individuals, there will be no ethical approval requirement. Findings will be disseminated in a peer-reviewed journal or presented at a conference.
PROSPERO REGISTRATION NUMBER: CRD42020176137.
OBJECTIVE: The aim of this study was to identify, review, map, and summarize findings from different types of literature reviews on the use of mobile health (mHealth) technologies to improve the uptake of cancer screening.
METHODS: The review methodology was guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Ovid MEDLINE, PyscINFO, and Embase were searched from inception to May 2021. The eligible criteria included reviews that focused on studies of interventions that used mobile phone devices to promote and deliver cancer screening and described the effectiveness or implementation of mHealth intervention outcomes. Key data fields such as study aims, types of cancer, mHealth formats, and outcomes were extracted, and the data were analyzed to address the objective of the review.
RESULTS: Our initial search identified 1981 titles, of which 12 (0.61%) reviews met the inclusion criteria (systematic reviews: n=6, 50%; scoping reviews: n=4, 33%; rapid reviews: n=1, 8%; narrative reviews: n=1, 8%). Most (57/67, 85%) of the interventions targeted breast and cervical cancer awareness and screening uptake. The most commonly used mHealth technologies for increasing cancer screening uptake were SMS text messages and telephone calls. Overall, mHealth interventions increased knowledge about screening and had high acceptance among participants. The likelihood of achieving improved uptake-related outcomes increased when interventions used >1 mode of communication (telephone reminders, physical invitation letters, and educational pamphlets) together with mHealth.
CONCLUSIONS: mHealth interventions increase cancer screening uptake, although multiple modes used in combination seem to be more effective.
METHODS: Women aged 40-74 years, from Segamat, Malaysia, with a mobile phone number, who participated in the South East Asian Community Observatory health survey, (2018) were randomized to an intervention (IG) or comparison group (CG). The IG received a multi-component mHealth intervention, i.e. information about BC was provided through a website, and telephone calls and text messages from community health workers (CHWs) were used to raise BC awareness and navigate women to CBE services. The CG received no intervention other than the usual option to access opportunistic screening. Regression analyses were conducted to investigate between-group differences over time in uptake of screening and variable influences on CBE screening participation.
RESULTS: We recruited 483 women in total; 122/225 from the IG and 144/258 from the CG completed the baseline and follow-up survey. Uptake of CBE by the IG was 45.8% (103/225) whilst 3.5% (5/144) of women from the CG who completed the follow-up survey reported that they attended a CBE during the study period (adjusted OR 37.21, 95% CI 14.13; 98.00, p<0.001). All IG women with a positive CBE attended a follow-up mammogram (11/11). Attendance by IG women was lower among women with a household income ≥RM 4,850 (adjusted OR 0.48, 95% CI 0.20; 0.95, p = 0.038) compared to participants with a household income
METHODS: Individual semi-structured interviews with 22 people (health professionals, cancer survivors, community volunteers and member from a non-governmental organization) and four focus group discussions (n = 22 participants) with women from a local community were conducted. All participants were purposively sampled and female residents registered with the South East Asia Community Observatory aged ≥40 years were eligible to participate in the focus group discussions. Data were transcribed verbatim and analyzed using thematic analysis.
RESULTS: The thematic analysis illuminated barriers, challenges and opportunities across six domains: (i) personal experiences and barriers to help-seeking as well as financial and travel access barriers; (ii) primary care challenges (related to delivering clinical breast examination and teaching breast-self-examination); (iii) secondary care challenges (related to mammogram services); (iv) disconnection between secondary and primary care breast cancer screening pathways; and (v) opportunities to improve breast cancer early detection relating to community civil service society activities (i.e. awareness raising, support groups, addressing stigma/embarrassment and encouraging husbands to support women) and vi) links between public healthcare personnel and community (i.e. improving breast self-examination education, clinical breast examination provision and subsidised mammograms).
CONCLUSION: The results point to a variety of reasons for low uptake and, therefore, to the complex nature of improving breast cancer screening and early detection. There is a need to adopt a systems approach to address this complexity and to take account of the socio-cultural context of communities in order, in turn, to strengthen cancer control policy and practices in Malaysia.
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: 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.
METHOD: Eleven focus group discussions (FGDs) were conducted with a purposive sample of 89 participants aged > 50 from the major ethnic groups in the Segamat District, Johor State. FGDs were audiotaped, transcribed verbatim, and translated into English. Data were analysed using thematic analysis.
RESULTS: We identified trust in doctors as a key reason for whether or not to seek health care. Generally, the participants had low awareness of CRC sign/symptoms and screening. Emotional and logistic concerns about sending a stool sample to a clinic emerged as the main barriers to screening. Simplified illustrated instructions about stool collection in Malay, Chinese and Tamil, free screening at health clinics and reminders to complete the iFOBT test were perceived to facilitate engagement in screening, and posited as strategies that were likely to increase iFOBT uptake.
CONCLUSION: Primary care physicians play a crucial role in terms of reducing patient's misperceptions, recommending screening to patients, enhancing attendance, and improving uptake of CRC screening. There is a need for further research to investigate ways in which to reduce identified barriers and implement and test potential facilitative strategies as well as examine adherence by doctors to clinical guidelines about CRC screening.
OBJECTIVE: Hence, we decided to translate the Hindi cognitive screening test battery (HCSTB) tool to Malayalam and establish the age and education-stratified norms for a Malayalam cognitive screening test battery (MCSTB).
MATERIAL AND METHODS: HCSTB was translated to Malayalam, back-translated by bilinguals conversant in Malayalam and English, and pretested on 30 older normal adults. Using a multistage sampling technique, we conducted a descriptive cross-sectional survey in the Thiruvananthapuram district of Kerala, India. We approached older adults aged ≥60 years for informed and written consent. We excluded subjects with depression, functional impairment, cognitive impairment, history of stroke, psychosis, and visual/hearing loss that impaired cognitive assessment.
RESULTS: The normative data were derived from 441 older adults: 226 (51%) from rural areas and 215 (49%) from urban areas. Age and education affected the cognitive scores. The time to administer MCSTB among normal adults was approximately 17 minutes.
DISCUSSION AND CONCLUSION: The derived normative data showed lower values than the published literature. A limitation of our study was the small number of older people with ≥12 years of education and the lack of neuroimaging of the subjects.
OBJECTIVES: This study aimed to critically evaluate and determine the effectiveness of educational interventions in improving pharmacogenomics knowledge and practice.
METHODS: Four electronic databases were searched: MEDLINE, EMBASE, CENTRAL, and PsycINFO. Studies on pharmacogenomics educational interventions for health care professionals and students with pre- and post-intervention assessments and results were included. No restrictions were placed on time, language, or educational contexts. The educational outcomes measured include both objective and subjective outcomes. The pharmacogenomics competency domains used to judge educational interventions are based on the competency domains listed by the American Association of Colleges of Pharmacies (AACP). The National Heart, Lung, and Blood Institute of the National Institutes of Health was used for the quality assessment of pre-post studies with no control group and the controlled intervention studies. No meta-analysis was conducted; the data were synthesized qualitatively. The systematic review was reported in accordance with the PRISMA statement.
RESULTS: Fifty studies were included in this review. All included studies integrated the AACP pharmacogenomics competency domains into their educational interventions. Most of the studies had educational interventions that integrated clinical cases (n = 44; 88%). Knowledge was the most frequently evaluated outcome (n = 34; 68%) and demonstrated significant improvement after the educational intervention that integrated AACP pharmacogenomics competency domains and employed active learning with clinical case inclusion.
CONCLUSION: This review provided evidence of the effectiveness of educational interventions in improving pharmacogenomics knowledge and practice. Incorporating pharmacogenomics competency domains into education and training, with patient cases for healthcare professionals and students, dramatically improved their pharmacogenomics knowledge, attitudes, and confidence in practice.
OBJECTIVE: This systematic review aimed to assess, in middle- and older-aged people, the relationship between dietary sodium intake and cognitive outcomes including cognitive function, risk of cognitive decline, or dementia.
METHODS: Six databases (PubMed, EMBASE, CINAHL, Psych info, Web of Science, and Cochrane Library) were searched from inception to 1 March 2020. Data extraction included information on study design, population characteristics, sodium reduction strategy (trials) or assessment of dietary sodium intake (observational studies), measurement of cognitive function or dementia, and summary of main results. Risk-of-bias assessments were performed using the National Heart, Lung, and Blood Institute (NHLBI) assessment tool.
RESULTS: Fifteen studies met the inclusion criteria including one clinical trial, six cohorts, and eight cross-sectional studies. Studies reported mixed associations between sodium levels and cognition. Results from the only clinical trial showed that a lower sodium intake was associated with improved cognition over six months. In analysis restricted to only high-quality studies, three out of four studies found that higher sodium intake was associated with impaired cognitive function.
CONCLUSION: There is some evidence that high salt intake is associated with poor cognition. However, findings are mixed, likely due to poor methodological quality, and heterogeneous dietary, analytical, and cognitive assessment methods and design of the studies. Reduced sodium intake may be a potential target for intervention. High quality prospective studies and clinical trials are needed.