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In recent years long-read technologies have moved from being a niche and specialist field to a point of relative maturity likely to feature frequently in the genomic landscape. Analogous to next generation sequencing, the cost of sequencing using long-read technologies has materially dropped whilst the instrument throughput continues to increase. Together these changes present the prospect of sequencing large numbers of individuals with the aim of fully characterizing genomes at highdoi:10.1093/hmg/ddy177 pmid:29767702 pmcid:PMC6061690 fatcat:hbdlke7jdndpnprwctbwss7qa4
more »... In this article, we will endeavour to present an introduction to long-read technologies showing: what long reads are; how they are distinct from short reads; why long reads are useful and how they are being used. We will highlight the recent developments in this field, and the applications and potential of these technologies in medical research, and clinical diagnostics and therapeutics.
Achieving vector control targets is a key step towards malaria elimination. Because of variations in reporting of progress towards vector control targets in 2013, the coverage of these vector control interventions in Namibia was assessed. Methods: Data on 9846 households, representing 41,314 people, collected in the 2013 nationally-representative Namibia Demographic and Health Survey were used to explore the coverage of two vector control methods: indoor residual spraying (IRS) anddoi:10.1186/s12936-018-2417-z pmid:30012154 pmcid:PMC6048889 fatcat:2vgtszg4vvem5nazpmbwbljgky
more »... reated nets (ITNs). Regional data on Plasmodium falciparum parasite rate in those aged 2-10 years (PfPR 2-10 ), obtained from the Malaria Atlas Project, were used to provide information on malaria transmission intensity. Poisson regression analyses were carried out exploring the relationship between household interventions and PfPR 2-10 , with fully adjusted models adjusting for wealth and residence type and accounting for regional and enumeration area clustering. Additionally, the coverage as a function of government intervention zones was explored and models were compared using log-likelihood ratio tests. Results: Intervention coverage was greatest in the highest transmission areas (PfPR 2-10 ≥ 5%), but was still below target levels of 95% coverage in these regions, with 27.6% of households covered by IRS, 32.3% with an ITN and 49.0% with at least one intervention (ITN and/or IRS). In fully adjusted models, PfPR 2-10 ≥ 5% was strongly associated with IRS (RR 14.54; 95% CI 5.56-38.02; p < 0.001), ITN ownership (RR 5.70; 95% CI 2.84-11.45; p < 0.001) and ITN and/or IRS coverage (RR 5.32; 95% CI 3.09-9.16; p < 0.001). Conclusions: The prevalence of IRS and ITN interventions in 2013 did not reflect the Namibian government intervention targets. As such, there is a need to include quantitative monitoring of such interventions to reliably inform intervention strategies for malaria elimination in Namibia.
Health insurance has been found to increase healthcare utilisation and reduce catastrophic health expenditures in a number of countries; however, coverage is often unequally distributed among populations. The sociodemographic patterns of health insurance in Namibia are not fully understood. We aimed to assess the prevalence of health insurance, the relation between health insurance and health service utilisation and to explore the sociodemographic factors associated with health insurance indoi:10.1186/s12939-019-0915-4 pmid:30670031 pmcid:PMC6341740 fatcat:exxxcsrnmvgthjty4ae6djvozq
more »... bia. Such findings may help to inform health policy to improve financial access to healthcare in the country.
A lthough many studies have examined metabolic syndrome (MetS) and the Framingham Risk Score (FRS), few studies have been carried out in African populations. This limited information on MetS and FRS leaves us with an incomplete understanding of the prevalence and distribution of risk of cardiometabolic disease in sub-Saharan Africa (SSA). It also prevents us from critically evaluating how each of the varying definitions of MetS compares in African populations. A clearer understanding of MetSdoi:10.2337/dc13-0739 pmid:23970722 pmcid:PMC3747938 fatcat:33k3zt2nxjg2tjz5hnu4jhqqde
more »... FRS in African populations may provide the basis for better identifying the impact of these definitions and tools on disease risk and, furthermore, help to evaluate the usefulness of such tools for research and for informing public health care and prevention policy in SSA. In this study, we examined the prevalence and distribution of MetS and high (.20%) FRS and compare the World Health Organization (WHO), Adult Treatment Panel (ATP) III, International Diabetes Federation (IDF), and the newly proposed harmonized definitions of MetS in a rural Ugandan population (1). A total of 8,087 participants, aged 13 years and older, were surveyed, of whom 7,423 (55% women) had complete data for analysis. Data were collected using standard procedures, and prediabetes was defined using HbA 1c $5.7% ($39 mmol/mol) (2). The prevalence of MetS varied by definition used, with the WHO, ATPIII, IDF, and harmonized definitions resulting in MetS prevalence of 4.1%, 9.9%, 8.9%, and 13.7%, respectively. The harmonized definition was the most sensitive, capturing all those identified using ATPIII and IDF and 85.4% of those identified using the WHO criteria. MetS increased with age (P value , 0.001), with a distinctive peak in the prevalence at ages 50-59 years for men for all definitions. The age-standardized prevalence of MetS, for all definitions, was higher in women (5.0% [95% CI 4.3-5.6] to 18.6% [95% CI 17.5-19.7]) than men (1.1% [95% CI 0.7-1.5] to 7.0% [95% CI 6.1-7.9]). The largest difference in MetS prevalence between men and women (1.1% [95% CI 0.7-1.5] vs. 14.5% [95% CI 13.5-15.6]) was found for the IDF definition. This was likely due to the substantial sex difference in central obesity (1.6% [95% CI 1.2-2.0] in men versus 29.7% [95% CI 28.4-31.0] in women), which is required in the IDF definition of MetS. Since there is no validated waist circumference cutoff for Africans, the IDF definition may currently be inappropriate for African populations (3). The mean value of FRS was 3.30 (SD 6.5), with 3.5% having high FRS. Only 8-28%, depending on the MetS definition, had both MetS and high FRS. By contrast to MetS, high FRS was more common among men (5.8% [95% CI 5.1-6.5]) than women (1.9% [95% CI 1.5-2.3]). High FRS increased with age (P value , 0.001). We found marked differences in the prevalence and distribution of cardiometabolic disease risk according to FRS and MetS definitions in this rural Ugandan population. These inconsistencies emphasize the need to more reliably assess the impact of these risk classifications in SSA populations. Prospective observational studies will be essential to evaluate and assess the distribution and determinants of cardiometabolic disease risk and to help to inform policy and health care programs in SSA (4).
S. Sandhu, R. Luben, and K. T. Khaw, unpublished data. ...pmid:12223442 fatcat:4kntxgmu3vbotbkqxwc74unxvi
In the article cited above, a minor error was made in the calculation of the Framingham Risk Score (FRS). The sentences "The mean value of FRS was 3.30 (SD 6.5), with 3.5% having high FRS. Only 8-28%, depending on the MetS definition, had both MetS and high FRS. By contrast to MetS, high FRS was more common among men (5.8% [95% CI 5.1-6.5]) than women (1.9% [95% CI 1.5-2.3])." should have read, "The mean value of FRS was 2.98 (SD 6.0), with 2.7% having high FRS. Only 5-14%, depending on thedoi:10.2337/dc14-er04 fatcat:lcir545dhbgwbdf5gbyulq5fqe
more »... definition, had both MetS and high FRS. By contrast to MetS, high FRS was more common among men (4.6% [95% CI 4.4-5.7]) than women (1.2% [95% CI 0.7-1.4])." This correction does not change the overall conclusions of the article.
OBJECTIVE Polygenic prediction of type 2 diabetes (T2D) in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of T2D from Africa and the poor transferability of European-derived polygenic risk scores (PRSs) in diverse ethnicities. We set out to evaluate if African American–, European-, or multiethnic-derived PRSs would improve polygenic prediction in continental Africans. RESEARCH DESIGN AND METHODS Using the PRSice software,doi:10.2337/dc21-0365 pmid:35015074 pmcid:PMC8918234 fatcat:iz5pwaqtpfga5ghgxwdgm77mpm
more »... c PRSs were computed with weights from the T2D GWAS multiancestry meta-analysis of 228,499 case and 1,178,783 control subjects. The South African Zulu study (n = 1,602 case and 981 control subjects) was used as the target data set. Validation and assessment of the best predictive PRS association with age at diagnosis were conducted in the Africa America Diabetes Mellitus (AADM) study (n = 2,148 case and 2,161 control subjects). RESULTS The discriminatory ability of the African American and multiethnic PRSs was similar. However, the African American–derived PRS was more transferable in all the countries represented in the AADM cohort and predictive of T2D in the country combined analysis compared with the European- and multiethnic-derived scores. Notably, participants in the 10th decile of this PRS had a 3.63-fold greater risk (odds ratio 3.63; 95% CI 2.19–4.03; P = 2.79 × 10−17) per risk allele of developing diabetes and were diagnosed 2.6 years earlier than those in the first decile. CONCLUSIONS African American–derived PRS enhances polygenic prediction of T2D in continental Africans. Improved representation of non-European populations (including Africans) in GWAS promises to provide better tools for precision medicine interventions in T2D.
S. ... Sandhu, PhD, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. E-mail: email@example.com Table II . ...doi:10.1016/j.jpeds.2017.07.049 pmid:29144273 pmcid:PMC5667719 fatcat:whtuhc734zfwzmbe7y7ufymuhm
BMJ Global Health
AbstractBackgroundDespite emerging evidence regarding the reversibility of stunting at older ages, most stunting research continues to focus on children below 5 years of age. We aimed to assess stunting prevalence and examine the sociodemographic distribution of stunting risk among older children and adolescents in a Malaysian population.MethodsWe used cross-sectional data on 6759 children and adolescents aged 6–19 years living in Segamat, Malaysia. We compared prevalence estimates for stuntingdoi:10.1017/gheg.2019.1 pmid:30891249 pmcid:PMC6415126 fatcat:iyxvhrrltvgt5gsbjflf4qg6jm
more »... defined using the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) references, using Cohen's κ coefficient. Associations between sociodemographic indices and stunting risk were examined using mixed-effects Poisson regression with robust standard errors.ResultsThe classification of children and adolescents as stunted or normal height differed considerably between the two references (CDC v. WHO; κ for agreement: 0.73), but prevalence of stunting was high regardless of reference (crude prevalence: CDC 29.2%; WHO: 19.1%). Stunting risk was approximately 19% higher among underweight v. normal weight children and adolescents (p = 0.030) and 21% lower among overweight children and adolescents (p = 0.001), and decreased strongly with improved household drinking water sources [risk ratio (RR) for water piped into house: 0.35, 95% confidence interval (95% CI) 0.30–0.41, p < 0.001). Protective effects were also observed for improved sanitation facilities (RR for flush toilet: 0.41, 95% CI 0.19–0.88, p = 0.023). Associations were not materially affected in multiple sensitivity analyses.ConclusionsOur findings justify a framework for strategies addressing stunting across childhood, and highlight the need for consensus on a single definition of stunting in older children and adolescents to streamline monitoring efforts.
There is little evidence regarding risk factors for child obesity in Asian populations, including the role of parental anthropometric and cardiometabolic risk factors. We examined the relation between parental risk factors and child obesity in a Malaysian population. Methods: We used data from health and demographic surveillance conducted by the South East Asia Community Observatory in Segamat, Malaysia. Analyses included 9207 individuals (4806 children, 2570 mothers and 1831 fathers). Childdoi:10.1093/ije/dyx114 pmid:29106558 pmcid:PMC5837730 fatcat:xy7tejd4qveexlqtgsa6b5tkzm
more »... sity was defined based on the World Health Organization 2007 reference. We assessed the relation between parental anthropometric (overweight, obesity and central obesity) and cardiometabolic (systolic hypertension, diastolic hypertension and hyperglycaemia) risk factors and child obesity, using mixed effects Poisson regression models with robust standard errors. Results: We found a high burden of overweight and obesity among children in this population (30% overweight or obese). Children of one or more obese parents had a 2-fold greater risk of being obese compared with children of non-obese parents. Sequential adjustment for parental and child characteristics did not materially affect estimates (fully adjusted relative risk for obesity in both parents: 2.39, 95% confidence interval: 1.82, 3.10, P < 0.001; P for trend < 0.001). These associations were not modified by parental or child sex. We found no consistent evidence for associations between parental cardiometabolic risk factors and child obesity. Conclusions: Parental obesity was strongly associated with child obesity in this population. Further exploration of the behavioural and environmental drivers of these associations may help inform strategies addressing child obesity in Asia. • This study adds to the limited evidence from Asia on the relation between parental anthropometric and cardiometabolic risk factors and child obesity. • Child obesity was independently associated with parental obesity in this population. There was no consistent evidence of associations between parental cardiometabolic risk factors and child obesity. • Associations observed were not modified by parental or child sex. • These findings have implications regarding the design of interventions to address child obesity in this region.
Recent research implicates antibiotic use as a potential contributor to child obesity risk. In this narrative review, we examine current observational evidence on the relation between antibiotic use in early childhood and subsequent measures of child body mass. We searched PubMed, Web of Science and the Cochrane Library to identify studies that assessed antibiotic exposure before 3 years of age and subsequent measures of body mass or risk of overweight or obesity in childhood. We identified 13doi:10.1017/gheg.2018.16 pmid:30410780 pmcid:PMC6218928 fatcat:dp6esa6pcvhq7kac7gcvvlzwpa
more »... tudies published before October 2017, based on a total of 6 81 332 individuals, which examined the relation between early life antibiotic exposure and measures of child body mass. Most studies did not appropriately account for confounding by indication for antibiotic use. Overall, we found no consistent and conclusive evidence of associations between early life antibiotic use and later child body mass [minimum overall adjusted odds ratio (aOR) reported: 1.01, 95% confidence interval (95% CI) 0.98-1.04, N = 2 60 556; maximum overall aOR reported: 2.56, 95% CI 1.36-4.79, N = 616], with no clinically meaningful increases in weight reported (maximum increase: 1.50 kg at 15 years of age). Notable methodological differences between studies, including variable measures of association and inclusion of confounders, limited more comprehensive interpretations. Evidence to date is insufficient to indicate that antibiotic use is an important risk factor for child obesity, or leads to clinically important differences in weight. Further comparable studies using routine clinical data may help clarify this association.
Men Women Cutoff S N (%) S P (%) Cutoff S N (%) S P (%) WC (cm) Hypertension Optimal $78 48.04 70.04 $85 34.02 80.17 Level 1 $94 7.01 98.93 $80 49.68 63.03 Level 2 $102 2.47 ... Lancet 2011; 378:804-814 Table 1 - 1 Optimal cutoff values for WC and BMI according to ROC analysis, including sensitivity (S N ) and specificity (S P ) for optimal and standard WC cutoffs Levels 1 and ...doi:10.2337/dc13-2096 pmid:24652731 fatcat:lrj7dydkhzfk3khlpfmuvgjd2i
Indian Heart Journal
Immediately after deflation of the cuff, a continuous 90 s recording of blood flow and vessel dimensions were obtained. ... The peak diameter of the artery was recorded 60 s after deflation of the cuff, and the percentage change from the baseline diameter was calculated. ...doi:10.1016/j.ihj.2018.01.030 pmid:30170643 pmcid:PMC6116724 fatcat:3s2nsdvj7zgl3gewqceoi7ed7i
Urban living is associated with unhealthy lifestyles that can increase the risk of cardiometabolic diseases. In sub-Saharan Africa (SSA), where the majority of people live in rural areas, it is still unclear if there is a corresponding increase in unhealthy lifestyles as rural areas adopt urban characteristics. This study examines the distribution of urban characteristics across rural communities in Uganda and their associations with lifestyle risk factors for chronic diseases. Methods anddoi:10.1371/journal.pmed.1001683 pmid:25072243 pmcid:PMC4114555 fatcat:hl6nfynpvveszeauc2pcyadjxy
more »... ngs: Using data collected in 2011, we examined cross-sectional associations between urbanicity and lifestyle risk factors in rural communities in Uganda, with 7,340 participants aged 13 y and above across 25 villages. Urbanicity was defined according to a multi-component scale, and Poisson regression models were used to examine associations between urbanicity and lifestyle risk factors by quartile of urbanicity. Despite all of the villages not having paved roads and running water, there was marked variation in levels of urbanicity across the villages, largely attributable to differences in economic activity, civil infrastructure, and availability of educational and healthcare services. In regression models, after adjustment for clustering and potential confounders including socioeconomic status, increasing urbanicity was associated with an increase in lifestyle risk factors such as physical inactivity (risk ratio [RR]: 1.19; 95% CI: 1.14, 1.24), low fruit and vegetable consumption (RR: 1.17; 95% CI: 1.10, 1.23), and high body mass index (RR: 1.48; 95% CI: 1.24, 1.77). Conclusions: This study indicates that even across rural communities in SSA, increasing urbanicity is associated with a higher prevalence of lifestyle risk factors for cardiometabolic diseases. This finding highlights the need to consider the health impact of urbanization in rural areas across SSA.
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