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Dalrymple U, Cameron E, Bhatt S, et al., 2017, Quantifying the contribution of plasmodium falciparum malaria to febrile illness amongst african children, eLife, Vol: 6
© Dalrymple et al. Suspected malaria cases in Africa increasingly receive a rapid diagnostic test (RDT) before antimalarials are prescribed. While this ensures efficient use of resources to clear parasites, the underlying cause of the individual’s fever remains unknown due to potential coinfection with a non-malarial febrile illness. Widespread use of RDTs does not necessarily prevent over-estimation of clinical malaria cases or sub-optimal case management of febrile patients. We present a new approach that allows inference of the spatiotemporal prevalence of both Plasmodium falciparum malaria-attributable and non-malarial fever in sub-Saharan African children from 2006 to 2014. We estimate that 35.7% of all self-reported fevers were accompanied by a malaria infection in 2014, but that only 28.0% of those (10.0% of all fevers) were causally attributable to malaria. Most fevers among malaria-positive children are therefore caused by non-malaria illnesses. This refined understanding can help improve interpretation of the burden of febrile illness and shape policy on fever case management.
Dalrymple U, Cameron E, Bhatt S, et al., 2017, Quantifying the contribution of <i>Plasmodium falciparum</i> malaria to febrile illness amongst African children, ELIFE, Vol: 6, ISSN: 2050-084X
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Pigott DM, Deshpande A, Letourneau I, et al., 2017, Local, national, and regional viral haemorrhagic fever pandemic potential in Africa: a multistage analysis., Lancet, Vol: 390, Pages: 2662-2672, ISSN: 0140-6736
BACKGROUND: Predicting when and where pathogens will emerge is difficult, yet, as shown by the recent Ebola and Zika epidemics, effective and timely responses are key. It is therefore crucial to transition from reactive to proactive responses for these pathogens. To better identify priorities for outbreak mitigation and prevention, we developed a cohesive framework combining disparate methods and data sources, and assessed subnational pandemic potential for four viral haemorrhagic fevers in Africa, Crimean-Congo haemorrhagic fever, Ebola virus disease, Lassa fever, and Marburg virus disease. METHODS: In this multistage analysis, we quantified three stages underlying the potential of widespread viral haemorrhagic fever epidemics. Environmental suitability maps were used to define stage 1, index-case potential, which assesses populations at risk of infection due to spillover from zoonotic hosts or vectors, identifying where index cases could present. Stage 2, outbreak potential, iterates upon an existing framework, the Index for Risk Management, to measure potential for secondary spread in people within specific communities. For stage 3, epidemic potential, we combined local and international scale connectivity assessments with stage 2 to evaluate possible spread of local outbreaks nationally, regionally, and internationally. FINDINGS: We found epidemic potential to vary within Africa, with regions where viral haemorrhagic fever outbreaks have previously occurred (eg, western Africa) and areas currently considered non-endemic (eg, Cameroon and Ethiopia) both ranking highly. Tracking transitions between stages showed how an index case can escalate into a widespread epidemic in the absence of intervention (eg, Nigeria and Guinea). Our analysis showed Chad, Somalia, and South Sudan to be highly susceptible to any outbreak at subnational levels. INTERPRETATION: Our analysis provides a unified assessment of potential epidemic trajectories, with the aim of allowing national
Fullman N, Barber RM, Abajobir AA, et al., 2017, Erratum:Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016 (The Lancet (2017) 390(10100) (1423–1459) (S014067361732336X)(10.1016/S0140-6736(17)32336-X)), The Lancet, Vol: 390, Pages: e23-e23, ISSN: 0140-6736
GBD 2016 SDG Collaborators. Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016. Lancet 2017; 390: 1423–59—In figure 8B of this Article (published Online First on Sept 12, 2017), the number of indicator targets has been changed from 1 to 9 for Turkmenistan, from 0 to 1 for Afghanistan, and from 1 to 2 for Yemen. Ettore Beghi, Neeraj Bhala, Hélène Carabin, Raimundas Lunevicius, Donald H Silberberg, and Caitlyn Steiner have been added to the list of GBD 2016 SDG Collaborators. Their affiliations, along with the affiliation of Soumya Swaminathan, have been added to the Affiliations section. These corrections have been made to the online version as of Sept 18, 2017, and the printed Article is correct.
Golding N, Burstein R, Longbottom J, et al., 2017, Mapping under-5 and neonatal mortality in Africa, 2000-15: a baseline analysis for the Sustainable Development Goals., Lancet, Vol: 390, Pages: 2171-2182, ISSN: 0140-6736
BACKGROUND: During the Millennium Development Goal (MDG) era, many countries in Africa achieved marked reductions in under-5 and neonatal mortality. Yet the pace of progress toward these goals substantially varied at the national level, demonstrating an essential need for tracking even more local trends in child mortality. With the adoption of the Sustainable Development Goals (SDGs) in 2015, which established ambitious targets for improving child survival by 2030, optimal intervention planning and targeting will require understanding of trends and rates of progress at a higher spatial resolution. In this study, we aimed to generate high-resolution estimates of under-5 and neonatal all-cause mortality across 46 countries in Africa. METHODS: We assembled 235 geographically resolved household survey and census data sources on child deaths to produce estimates of under-5 and neonatal mortality at a resolution of 5 × 5 km grid cells across 46 African countries for 2000, 2005, 2010, and 2015. We used a Bayesian geostatistical analytical framework to generate these estimates, and implemented predictive validity tests. In addition to reporting 5 × 5 km estimates, we also aggregated results obtained from these estimates into three different levels-national, and subnational administrative levels 1 and 2-to provide the full range of geospatial resolution that local, national, and global decision makers might require. FINDINGS: Amid improving child survival in Africa, there was substantial heterogeneity in absolute levels of under-5 and neonatal mortality in 2015, as well as the annualised rates of decline achieved from 2000 to 2015. Subnational areas in countries such as Botswana, Rwanda, and Ethiopia recorded some of the largest decreases in child mortality rates since 2000, positioning them well to achieve SDG targets by 2030 or earlier. Yet these places were the exception for Africa, since many areas, particularly in central and western Africa, must reduce unde
GBD 2016 Disease and Injury Incidence and Prevalence Collaborators, 2017, Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016., Lancet, Vol: 390, Pages: 1211-1259, ISSN: 0140-6736
BACKGROUND: As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016. METHODS: We estimated prevalence and incidence for 328 diseases and injuries and 2982 sequelae, their non-fatal consequences. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between incidence, prevalence, remission, and cause of death rates for each condition. For some causes, we used alternative modelling strategies if incidence or prevalence needed to be derived from other data. YLDs were estimated as the product of prevalence and a disability weight for all mutually exclusive sequelae, corrected for comorbidity and aggregated to cause level. We updated the Socio-demographic Index (SDI), a summary indicator of income per capita, years of schooling, and total fertility rate. GBD 2016 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). FINDINGS: Globally, low back pain, migraine, age-related and other hearing loss, iron-deficiency anaemia, and major depressive disorder were the five leading causes of YLDs in 2016, contributing 57·6 million (95% uncertainty interval [UI] 40·8-75·9 million [7·2%, 6·0-8·3]), 45·1 million (29·0-62·8 million [5·6%, 4·0-7·2]), 36·3 million (25·3-50·9 million [4·5%, 3·8-5·3]), 34·7 million (23·0-49·6 million [4·3%, 3·5-5·2]), and 34·1 million (23·5-46·0 million [4·2%, 3·2-5·3]) of total YLDs
Hay SI, Abajobir AA, Abate KH, et al., 2017, Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016, Lancet, Vol: 390, Pages: 1260-1344, ISSN: 0140-6736
BackgroundMeasurement of changes in health across locations is useful to compare and contrast changing epidemiological patterns against health system performance and identify specific needs for resource allocation in research, policy development, and programme decision making. Using the Global Burden of Diseases, Injuries, and Risk Factors Study 2016, we drew from two widely used summary measures to monitor such changes in population health: disability-adjusted life-years (DALYs) and healthy life expectancy (HALE). We used these measures to track trends and benchmark progress compared with expected trends on the basis of the Socio-demographic Index (SDI).MethodsWe used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2016. We calculated DALYs by summing years of life lost and years of life lived with disability for each location, age group, sex, and year. We estimated HALE using age-specific death rates and years of life lived with disability per capita. We explored how DALYs and HALE differed from expected trends when compared with the SDI: the geometric mean of income per person, educational attainment in the population older than age 15 years, and total fertility rate.FindingsThe highest globally observed HALE at birth for both women and men was in Singapore, at 75·2 years (95% uncertainty interval 71·9–78·6) for females and 72·0 years (68·8–75·1) for males. The lowest for females was in the Central African Republic (45·6 years [42·0–49·5]) and for males was in Lesotho (41·5 years [39·0–44·0]). From 1990 to 2016, global HALE increased by an average of 6·24 years (5·97–6·48) for both sexes combined. Global HALE increased by 6·04 years (
Naghavi M, Abajobir AA, Abbafati C, et al., 2017, Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016, Lancet, Vol: 390, Pages: 1151-1210, ISSN: 0140-6736
BackgroundMonitoring levels and trends in premature mortality is crucial to understanding how societies can address prominent sources of early death. The Global Burden of Disease 2016 Study (GBD 2016) provides a comprehensive assessment of cause-specific mortality for 264 causes in 195 locations from 1980 to 2016. This assessment includes evaluation of the expected epidemiological transition with changes in development and where local patterns deviate from these trends.MethodsWe estimated cause-specific deaths and years of life lost (YLLs) by age, sex, geography, and year. YLLs were calculated from the sum of each death multiplied by the standard life expectancy at each age. We used the GBD cause of death database composed of: vital registration (VR) data corrected for under-registration and garbage coding; national and subnational verbal autopsy (VA) studies corrected for garbage coding; and other sources including surveys and surveillance systems for specific causes such as maternal mortality. To facilitate assessment of quality, we reported on the fraction of deaths assigned to GBD Level 1 or Level 2 causes that cannot be underlying causes of death (major garbage codes) by location and year. Based on completeness, garbage coding, cause list detail, and time periods covered, we provided an overall data quality rating for each location with scores ranging from 0 stars (worst) to 5 stars (best). We used robust statistical methods including the Cause of Death Ensemble model (CODEm) to generate estimates for each location, year, age, and sex. We assessed observed and expected levels and trends of cause-specific deaths in relation to the Socio-demographic Index (SDI), a summary indicator derived from measures of average income per capita, educational attainment, and total fertility, with locations grouped into quintiles by SDI. Relative to GBD 2015, we expanded the GBD cause hierarchy by 18 causes of death for GBD 2016.FindingsThe quality of available data varied by lo
Fullman N, Barbar RM, Abajobir AA, et al., 2017, Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016, The Lancet, Vol: 390, Pages: 1423-1459, ISSN: 0140-6736
BackgroundThe UN's Sustainable Development Goals (SDGs) are grounded in the global ambition of “leaving no one behind”. Understanding today's gains and gaps for the health-related SDGs is essential for decision makers as they aim to improve the health of populations. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016), we measured 37 of the 50 health-related SDG indicators over the period 1990–2016 for 188 countries, and then on the basis of these past trends, we projected indicators to 2030.MethodsWe used standardised GBD 2016 methods to measure 37 health-related indicators from 1990 to 2016, an increase of four indicators since GBD 2015. We substantially revised the universal health coverage (UHC) measure, which focuses on coverage of essential health services, to also represent personal health-care access and quality for several non-communicable diseases. We transformed each indicator on a scale of 0–100, with 0 as the 2·5th percentile estimated between 1990 and 2030, and 100 as the 97·5th percentile during that time. An index representing all 37 health-related SDG indicators was constructed by taking the geometric mean of scaled indicators by target. On the basis of past trends, we produced projections of indicator values, using a weighted average of the indicator and country-specific annualised rates of change from 1990 to 2016 with weights for each annual rate of change based on out-of-sample validity. 24 of the currently measured health-related SDG indicators have defined SDG targets, against which we assessed attainment.FindingsGlobally, the median health-related SDG index was 56·7 (IQR 31·9–66·8) in 2016 and country-level performance markedly varied, with Singapore (86·8, 95% uncertainty interval 84·6–88·9), Iceland (86·0, 84·1–87·6), and Sweden (85·6, 81·8–87·8) having the h
Bhatt S, Cameron E, Flaxman SR, et al., 2017, Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization., Interface, Vol: 14, ISSN: 1742-5662
Maps of infectious disease-charting spatial variations in the force of infection, degree of endemicity and the burden on human health-provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is Gaussian process regression, a mathematical framework composed of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean. Though many techniques have been developed to improve the flexibility and fitting of the covariance function, models for the mean function have typically been restricted to simple linear terms. For infectious diseases, known to be driven by complex interactions between environmental and socio-economic factors, improved modelling of the mean function can greatly boost predictive power. Here, we present an ensemble approach based on stacked generalization that allows for multiple nonlinear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We apply this method to mapping Plasmodium falciparum prevalence data in sub-Saharan Africa and show that the generalized ensemble approach markedly outperforms any individual method.
Bhatt S, Heda NL, Kumar K, et al., 2017, Study of electronic structure and Compton profiles of transition metal diborides, Physica B: Condensed Matter, Vol: 518, Pages: 13-19, ISSN: 0921-4526
Patouillard E, Ghani ACH, Bhatt S, et al., 2017, Global investment targets for malaria control and elimination between 2016 and 2030, BMJ Global Health, Vol: 2, ISSN: 2059-7908
Background Access to malaria control interventions falls short of universal health coverage. The Global Technical Strategy for malaria targets at least 90% reduction in case incidence and mortality rates, and elimination in 35 countries by 2030. The potential to reach these targets will be determined in part by investments in malaria. This study estimates the financing required for malaria control and elimination over the 2016–2030 period.Methods A mathematical transmission model was used to explore the impact of increasing intervention coverage on burden and costs. The cost analysis took a public provider perspective covering all 97 malaria endemic countries and territories in 2015. All control interventions currently recommended by the WHO were considered. Cost data were sourced from procurement databases, the peer-reviewed literature, national malaria strategic plans, the WHO-CHOICE project and key informant interviews.Results Annual investments of $6.4 billion (95% uncertainty interval (UI $4.5–$9.0 billion)) by 2020, $7.7 billion (95% UI $5.4–$10.9 billion) by 2025 and $8.7 billion (95% UI $6.0–$12.3 billion) by 2030 will be required to reach the targets set in the Global Technical Strategy. These are equivalent to annual investment per person at risk of malaria of US$3.90 by 2020, US$4.30 by 2025 and US$4.40 by 2030, compared with US$2.30 if interventions were sustained at current coverage levels. The 20 countries with the highest burden in 2015 will require 88% of the total investment.Conclusions Given the challenges in increasing domestic and international funding, the efficient use of currently available resources should be a priority
Bennett A, Bisanzio D, Yukich JO, et al., 2017, Population coverage of artemisinin-based combination treatment in children younger than 5 years with fever and Plasmodium falciparum infection in Africa, 2003-2015: a modelling study using data from national surveys., Lancet Global Health, Vol: 5, Pages: e418-e427, ISSN: 2214-109X
BACKGROUND: Artemisinin-based combination therapies (ACTs) are the most effective treatment for uncomplicated Plasmodium falciparum malaria infection. A commonly used indicator for monitoring and assessing progress in coverage of malaria treatment is the proportion of children younger than 5 years with reported fever in the previous 14 days who have received an ACT. We propose an improved indicator that incorporates parasite infection status (as assessed by a rapid diagnostic test [RDT]), which is available in recent household surveys. In this study we estimated the annual proportion of children younger than 5 years with fever and a positive RDT in Africa who received an ACT in 2003-15. METHODS: Our modelling study used cross-sectional data on treatment for fever and RDT status for children younger than 5 years compiled from all nationally available representative household surveys (the Malaria Indicator Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys) across sub-Saharan Africa between 2003 and 2015. Estimates for the proportion of children younger than 5 years with a fever within the previous 14 days and P falciparum infection assessed by RDT who received an ACT were incorporated in a generalised additive mixed model, including data on ACT distributions, to estimate coverage across all countries and time periods. We did random effects meta-analyses to examine individual, household, and community effects associated with ACT coverage. FINDINGS: We obtained data on 201 704 children younger than 5 years from 103 surveys (22 MIS, 61 DHS, and 20 MICS) across 33 countries. RDT results were available for 40 of these surveys including 40 261 (20%) children, and we predicted RDT status for the remaining 161 443 (80%) children. Our results showed that ACT coverage in children younger than 5 years with a fever and P falciparum infection increased across sub-Saharan Africa in 2003-15, but even in 2015, only 19·7% (95% CI 15·6-24&mid
Wiebe A, Longbottom J, Gleave K, et al., 2017, Geographical distributions of African malaria vector sibling species and evidence for insecticide resistance, MALARIA JOURNAL, Vol: 16, ISSN: 1475-2875
Background:Many of the mosquito species responsible for malaria transmission belong to a sibling complex; a taxonomic group of morphologically identical, closely related species. Sibling species often differ in several important factors that have the potential to impact malaria control, including their geographical distribution, resistance to insecticides, biting and resting locations, and host preference. The aim of this study was to define the geographical distributions of dominant malaria vector sibling species in Africa so these distributions can be coupled with data on key factors such as insecticide resistance to aid more focussed, species-selective vector control.Results:Within the Anopheles gambiae species complex and the Anopheles funestus subgroup, predicted geographical distributions for Anopheles coluzzii, An. gambiae (as now defined) and An. funestus (distinct from the subgroup) have been produced for the first time. Improved predicted geographical distributions for Anopheles arabiensis, Anopheles melas and Anopheles merus have been generated based on records that were confirmed using molecular identification methods and a model that addresses issues of sampling bias and past changes to the environment. The data available for insecticide resistance has been evaluated and differences between sibling species are apparent although further analysis is required to elucidate trends in resistance.Conclusions:Sibling species display important variability in their geographical distributions and the most important malaria vector sibling species in Africa have been mapped here for the first time. This will allow geographical occurrence data to be coupled with species-specific data on important factors for vector control including insecticide resistance. Species-specific data on insecticide resistance is available for the most important malaria vectors in Africa, namely An. arabiensis, An. coluzzii, An. gambiae and An. funestus. Future work to combine these data wi
Hamilton M, Mahiane G, Werst E, et al., 2017, Spectrum-Malaria: a user-friendly projection tool for health impact assessment and strategic planning by malaria control programmes in sub-Saharan Africa, Malaria Journal, Vol: 16, ISSN: 1475-2875
Background:Scale-up of malaria prevention and treatment needs to continue but national strategies and budget allocations are not always evidence-based. This article presents a new modelling tool projecting malaria infection, cases and deaths to support impact evaluation, target setting and strategic planning.Methods:Nested in the Spectrum suite of programme planning tools, the model includes historic estimates of case incidence and deaths in groups aged up to 4, 5–14, and 15+ years, and prevalence of Plasmodium falciparum infection (PfPR) among children 2–9 years, for 43 sub-Saharan African countries and their 602 provinces, from the WHO and malaria atlas project. Impacts over 2016–2030 are projected for insecticide-treated nets (ITNs), indoor residual spraying (IRS), seasonal malaria chemoprevention (SMC), and effective management of uncomplicated cases (CMU) and severe cases (CMS), using statistical functions fitted to proportional burden reductions simulated in the P. falciparum dynamic transmission model OpenMalaria.Results:In projections for Nigeria, ITNs, IRS, CMU, and CMS scale-up reduced health burdens in all age groups, with largest proportional and especially absolute reductions in children up to 4 years old. Impacts increased from 8 to 10 years following scale-up, reflecting dynamic effects. For scale-up of each intervention to 80% effective coverage, CMU had the largest impacts across all health outcomes, followed by ITNs and IRS; CMS and SMC conferred additional small but rapid mortality impacts.Discussion:Spectrum-Malaria’s user-friendly interface and intuitive display of baseline data and scenario projections holds promise to facilitate capacity building and policy dialogue in malaria programme prioritization. The module’s linking to the OneHealth Tool for costing will support use of the software for strategic budget allocation. In settings with moderately low coverage levels, such as Nigeria, improving case management an
Wang H, Bhutta ZA, Coates MM, et al., 2017, Erratum: Global, regional, national, and selected subnational levels of stillbirths, neonatal, infant, and under-5 mortality, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015 (The Lancet (2016) 388(10053) (1725–1774)(S0140673616315756)(10.1016/S0140-6736(16)31575-6)), The Lancet, Vol: 389, Pages: e1-e1, ISSN: 0140-6736
© 2017 Elsevier Ltd GBD 2015 Child Mortality Collaborators. Global, regional, national, and selected subnational levels of stillbirths, neonatal, infant, and under-5 mortality, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016; 388: 1725–74—In this Article, Mohsen Naghavi, Michael J Kutz, Chantal Huynh, Samer Hamidi, Addisu Shunu Beyene, and Stephen S Lim should have been listed as authors. The affiliation details for Simon I Hay have been updated. The funding statement for Simon I Hay has been added. These corrections have been made to the online version as of Jan 5, 2017.
Tusting LS, Bisanzio D, Gibson H, et al., 2017, THE EFFECT OF HOUSING IMPROVEMENTS ON MALARIA IN AFRICA, 2000-2015, 66th Annual Meeting of the American-Society-of-Tropical-Medicine-and-Hygiene (ASTMH), Publisher: AMER SOC TROP MED & HYGIENE, Pages: 504-504, ISSN: 0002-9637
Routledge I, Chevez JER, Cucunuba Z, et al., 2017, UNDERSTANDING THE MALARIA TRANSMISSION PROCESS IN NEAR-ELIMINATION SETTINGS, 66th Annual Meeting of the American-Society-of-Tropical-Medicine-and-Hygiene (ASTMH), Publisher: AMER SOC TROP MED & HYGIENE, Pages: 332-332, ISSN: 0002-9637
Gething PW, Casey DC, Weiss DJ, et al., 2016, Mapping Plasmodium falciparum mortality in Africa between 1990 and 2015, New England Journal of Medicine, Vol: 375, Pages: 2435-2445, ISSN: 1533-4406
BACKGROUND:Malaria control has not been routinely informed by the assessment of subnationalvariation in malaria deaths. We combined data from the Malaria Atlas Project andthe Global Burden of Disease Study to estimate malaria mortality across subSaharanAfrica on a grid of 5 km2 from 1990 through 2015.METHODS:We estimated malaria mortality using a spatiotemporal modeling framework ofgeolocated data (i.e., with known latitude and longitude) on the clinical incidenceof malaria, coverage of antimalarial drug treatment, case fatality rate, and populationdistribution according to age.RESULTS:Across sub-Saharan Africa during the past 15 years, we estimated that there wasan overall decrease of 57% (95% uncertainty interval, 46 to 65) in the rate of malariadeaths, from 12.5 (95% uncertainty interval, 8.3 to 17.0) per 10,000 populationin 2000 to 5.4 (95% uncertainty interval, 3.4 to 7.9) in 2015. This led to an overalldecrease of 37% (95% uncertainty interval, 36 to 39) in the number of malariadeaths annually, from 1,007,000 (95% uncertainty interval, 666,000 to 1,376,000)to 631,000 (95% uncertainty interval, 394,000 to 914,000). The share of malariadeaths among children younger than 5 years of age ranged from more than 80%at a rate of death of more than 25 per 10,000 to less than 40% at rates below 1 per10,000. Areas with high malaria mortality (>10 per 10,000) and low coverage (<50%)of insecticide-treated bed nets and antimalarial drugs included much of Nigeria,Angola, and Cameroon and parts of the Central African Republic, Congo, Guinea,and Equatorial Guinea.CONCLUSIONS:We estimated that there was an overall decrease of 57% in the rate of death frommalaria across sub-Saharan Africa over the past 15 years and identified severalcountries in which high rates of death were associated with low coverage of antimalarialtreatment and prevention programs.
Wang H, Bhutta ZA, Coates MM, et al., 2016, Global, regional, national, and selected subnational levels of stillbirths, neonatal, infant, and under-5 mortality, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015, LANCET, Vol: 388, Pages: 1725-1774, ISSN: 0140-6736
Background Established in 2000, Millennium Development Goal 4 (MDG4) catalysed extraordinary political,fi nancial, and social commitments to reduce under-5 mortality by two-thirds between 1990 and 2015. At thecountry level, the pace of progress in improving child survival has varied markedly, highlighting a crucial need tofurther examine potential drivers of accelerated or slowed decreases in child mortality. The Global Burden ofDisease 2015 Study (GBD 2015) provides an analytical framework to comprehensively assess these trends forunder-5 mortality, age-specifi c and cause-specifi c mortality among children under 5 years, and stillbirths bygeography over time.Methods Drawing from analytical approaches developed and refi ned in previous iterations of the GBD study, wegenerated updated estimates of child mortality by age group (neonatal, post-neonatal, ages 1–4 years, and under 5)for 195 countries and territories and selected subnational geographies, from 1980–2015. We also estimated numbersand rates of stillbirths for these geographies and years. Gaussian process regression with data source adjustmentsfor sampling and non-sampling bias was applied to synthesise input data for under-5 mortality for each geography.Age-specifi c mortality estimates were generated through a two-stage age–sex splitting process, and stillbirthestimates were produced with a mixed-eff ects model, which accounted for variable stillbirth defi nitions and datasource-specifi c biases. For GBD 2015, we did a series of novel analyses to systematically quantify the drivers oftrends in child mortality across geographies. First, we assessed observed and expected levels and annualised ratesof decrease for under-5 mortality and stillbirths as they related to the Soci-demographic Index (SDI). Second, weexamined the ratio of recorded and expected levels of child mortality, on the basis of SDI, across geographies, aswell as diff erences in recorded and expected annualised rates of chang
Kassebaum NJ, Arora M, Barber RM, et al., 2016, Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015, The Lancet, Vol: 388, Pages: 1603-1658, ISSN: 0140-6736
SummaryBackground Healthy life expectancy (HALE) and disability-adjusted life-years (DALYs) provide summary measures of health across geographies and time that can inform assessments of epidemiological patterns and health system performance, help to prioritise investments in research and development, and monitor progress toward the Sustainable Development Goals (SDGs). We aimed to provide updated HALE and DALYs for geographies worldwide and evaluate how disease burden changes with development. Methods We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2015. We calculated DALYs by summing years of life lost (YLLs) and years of life lived with disability (YLDs) for each geography, age group, sex, and year. We estimated HALE using the Sullivan method, which draws from age-specific death rates and YLDs per capita. We then assessed how observed levels of DALYs and HALE differed from expected trends calculated with the Socio-demographic Index (SDI), a composite indicator constructed from measures of income per capita, average years of schooling, and total fertility rate. Findings Total global DALYs remained largely unchanged from 1990 to 2015, with decreases in communicable, neonatal, maternal, and nutritional (Group 1) disease DALYs offset by increased DALYs due to non-communicable diseases (NCDs). Much of this epidemiological transition was caused by changes in population growth and ageing, but it was accelerated by widespread improvements in SDI that also correlated strongly with the increasing importance of NCDs. Both total DALYs and age-standardised DALY rates due to most Group 1 causes significantly decreased by 2015, and although total burden climbed for the majority of NCDs, age-standardised DALY rates due to NCDs declined. Nonetheless, age-standardised DALY
Vos T, Allen C, Arora M, et al., 2016, Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015, The Lancet, Vol: 388, Pages: 1545-1602, ISSN: 0140-6736
Background Non-fatal outcomes of disease and injury increasingly detract from the ability of the world's population to live in full health, a trend largely attributable to an epidemiological transition in many countries from causes affecting children, to non-communicable diseases (NCDs) more common in adults. For the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015), we estimated the incidence, prevalence, and years lived with disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015. Methods We estimated incidence and prevalence by age, sex, cause, year, and geography with a wide range of updated and standardised analytical procedures. Improvements from GBD 2013 included the addition of new data sources, updates to literature reviews for 85 causes, and the identification and inclusion of additional studies published up to November, 2015, to expand the database used for estimation of non-fatal outcomes to 60 900 unique data sources. Prevalence and incidence by cause and sequelae were determined with DisMod-MR 2.1, an improved version of the DisMod-MR Bayesian meta-regression tool first developed for GBD 2010 and GBD 2013. For some causes, we used alternative modelling strategies where the complexity of the disease was not suited to DisMod-MR 2.1 or where incidence and prevalence needed to be determined from other data. For GBD 2015 we created a summary indicator that combines measures of income per capita, educational attainment, and fertility (the Socio-demographic Index [SDI]) and used it to compare observed patterns of health loss to the expected pattern for countries or locations with similar SDI scores. Findings We generated 9·3 billion estimates from the various combinations of prevalence, incidence, and YLDs for causes, sequelae, and impairments by age, sex, geography, and year. In 2015, two causes had acute incidences in excess of 1 billion: upper respiratory infections (
Forouzanfar MH, Afshin A, Alexander LT, et al., 2016, Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015, The Lancet, Vol: 388, Pages: 1659-1724, ISSN: 0140-6736
SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated
GBD 2015 Collaborators, Piel FBJ, 2016, Measuring the health-related Sustainable Development Goals in 188 countries: a baseline analysis from the Global Burden of Disease Study 2015, The Lancet, Vol: 388, Pages: 1813-1850, ISSN: 0140-6736
BackgroundIn September, 2015, the UN General Assembly established the Sustainable Development Goals (SDGs). The SDGs specify 17 universal goals, 169 targets, and 230 indicators leading up to 2030. We provide an analysis of 33 health-related SDG indicators based on the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015).MethodsWe applied statistical methods to systematically compiled data to estimate the performance of 33 health-related SDG indicators for 188 countries from 1990 to 2015. We rescaled each indicator on a scale from 0 (worst observed value between 1990 and 2015) to 100 (best observed). Indices representing all 33 health-related SDG indicators (health-related SDG index), health-related SDG indicators included in the Millennium Development Goals (MDG index), and health-related indicators not included in the MDGs (non-MDG index) were computed as the geometric mean of the rescaled indicators by SDG target. We used spline regressions to examine the relations between the Socio-demographic Index (SDI, a summary measure based on average income per person, educational attainment, and total fertility rate) and each of the health-related SDG indicators and indices.FindingsIn 2015, the median health-related SDG index was 59·3 (95% uncertainty interval 56·8–61·8) and varied widely by country, ranging from 85·5 (84·2–86·5) in Iceland to 20·4 (15·4–24·9) in Central African Republic. SDI was a good predictor of the health-related SDG index (r2=0·88) and the MDG index (r2=0·92), whereas the non-MDG index had a weaker relation with SDI (r2=0·79). Between 2000 and 2015, the health-related SDG index improved by a median of 7·9 (IQR 5·0–10·4), and gains on the MDG index (a median change of 10·0 [6·7–13·1]) exceeded that of the non-MDG index (a median change of 5·5 [2·1–8·
Shearer FM, Huang Z, Weiss DJ, et al., 2016, Estimating Geographical Variation in the Risk of Zoonotic Plasmodium knowlesi Infection in Countries Eliminating Malaria., PLOS Neglected Tropical Diseases, Vol: 10, ISSN: 1935-2735
BACKGROUND: Infection by the simian malaria parasite, Plasmodium knowlesi, can lead to severe and fatal disease in humans, and is the most common cause of malaria in parts of Malaysia. Despite being a serious public health concern, the geographical distribution of P. knowlesi malaria risk is poorly understood because the parasite is often misidentified as one of the human malarias. Human cases have been confirmed in at least nine Southeast Asian countries, many of which are making progress towards eliminating the human malarias. Understanding the geographical distribution of P. knowlesi is important for identifying areas where malaria transmission will continue after the human malarias have been eliminated. METHODOLOGY/PRINCIPAL FINDINGS: A total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. Predictions were then made throughout Southeast Asia, including regions where infection data are sparse. The resulting map predicts areas of high risk for P. knowlesi infection in a number of countries that are forecast to be malaria-free by 2025 (Malaysia, Cambodia, Thailand and Vietnam) as well as countries projected to be eliminating malaria (Myanmar, Laos, Indonesia and the Philippines). CONCLUSIONS/SIGNIFICANCE: We have produced the first map of P. knowlesi malaria risk, at a fine-scale resolution, to identify priority areas for surveillance based on regions with sparse data and high estimated risk. Our map provides an initial evidence base to better understand the spatial distribution of this disease and its potential wider contribution to malaria incidence. Considering malaria elimination goals, areas for prioritised surveillance are identified.
Bhatt S, Kumar K, Arora G, et al., 2016, High energy Compton spectroscopy and electronic structure of Laves phase ZrFe 2, Radiation Physics and Chemistry, Vol: 125, Pages: 109-114, ISSN: 0969-806X
Pigott DM, Millear AI, Earl L, et al., 2016, Updates to the zoonotic niche map of Ebola virus disease in Africa, eLife, Vol: 5, ISSN: 2050-084X
As the outbreak of Ebola virus disease (EVD) in West Africa is now contained, attention is turning from control to future outbreak prediction and prevention. Building on a previously published zoonotic niche map (Pigott et al., 2014), this study incorporates new human and animal occurrence data and expands upon the way in which potential bat EVD reservoir species are incorporated. This update demonstrates the potential for incorporating and updating data used to generate the predicted suitability map. A new data portal for sharing such maps is discussed. This output represents the most up-to-date estimate of the extent of EVD zoonotic risk in Africa. These maps can assist in strengthening surveillance and response capacity to contain viral haemorrhagic fevers.
Hallett TB, Anderson S-J, Asante CA, et al., 2016, Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning, AIDS, Vol: 30, Pages: 1467-1474, ISSN: 0269-9370
Objective: There is evidence of substantial subnational variation in the HIV epidemic. However, robust spatial HIV data are often only available at high levels of geographic aggregation and not at the finer resolution needed for decision making. Therefore, spatial analysis methods that leverage available data to provide local estimates of HIV prevalence may be useful. Such methods exist but have not been formally compared when applied to HIV.Design/methods: Six candidate methods – including those used by the Joint United Nations Programme on HIV/AIDS to generate maps and a Bayesian geostatistical approach applied to other diseases – were used to generate maps and subnational estimates of HIV prevalence across three countries using cluster level data from household surveys. Two approaches were used to assess the accuracy of predictions: internal validation, whereby a proportion of input data is held back (test dataset) to challenge predictions; and comparison with location-specific data from household surveys in earlier years.Results: Each of the methods can generate usefully accurate predictions of prevalence at unsampled locations, with the magnitude of the error in predictions similar across approaches. However, the Bayesian geostatistical approach consistently gave marginally the strongest statistical performance across countries and validation procedures.Conclusions: Available methods may be able to furnish estimates of HIV prevalence at finer spatial scales than the data currently allow. The subnational variation revealed can be integrated into planning to ensure responsiveness to the spatial features of the epidemic. The Bayesian geostatistical approach is a promising strategy for integrating HIV data to generate robust local estimates.
Ahuja BL, Sharma S, Heda NL, et al., 2016, Electronic and optical properties of ceramic Sc2O3 and Y2O3: Compton spectroscopy and first principles calculations, Journal of Physics and Chemistry of Solids, Vol: 92, Pages: 53-63, ISSN: 0022-3697
Meena BS, Heda NL, Kumar K, et al., 2016, Compton profiles and Mulliken’s populations of cobalt, nickel and copper tungstates: Experiment and theory, Physica B: Condensed Matter, Vol: 484, Pages: 1-6, ISSN: 0921-4526
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