Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Background Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings In 2021, there were 529 million (95% uncertainty interval [UI] 500–564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9]) and, at the regional level, in Oceania (12·3% [11·5–13·0]). Nationally, Qatar had the world’s highest age-specific prevalence of diabetes, at 76·1% (73·1–79·5) in individuals aged 75–79 years. Total diabetes prevalence—especially among older adults—primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases and 95·4% (94·9–95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5–71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5–30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%.Peer ReviewedPostprint (published version

Fifty Years of Innovation in Plastic Surgery

Background Innovation has molded the current landscape of plastic surgery. However, documentation of this process only exists scattered throughout the literature as individual articles. The few attempts made to profile innovation in plastic surgery have been narrative, and therefore qualitative and inherently biased. Through the implementation of a novel innovation metric, this work aims to identify and characterise the most prevalent innovations in plastic surgery over the last 50 years. Methods Patents and publications related to plastic surgery (1960 to 2010) were retrieved from patent and MEDLINE databases, respectively. The most active patent codes were identified and grouped into technology areas, which were subsequently plotted graphically against publication data. Expert-derived technologies outside of the top performing patents areas were additionally explored. Results Between 1960 and 2010, 4,651 patents and 43,118 publications related to plastic surgery were identified. The most active patent codes were grouped under reconstructive prostheses, implants, instruments, non-invasive techniques, and tissue engineering. Of these areas and other expert-derived technologies, those currently undergoing growth include surgical instruments, implants, non-invasive practices, transplantation and breast surgery. Innovations related to microvascular surgery, liposuction, tissue engineering, lasers and prostheses have all plateaued. Conclusions The application of a novel metric for evaluating innovation quantitatively outlines the natural history of technologies fundamental to the evolution of plastic surgery. Analysis of current innovation trends provides some insight into which technology domains are the most active

Force over time for the initial six 30 second periods of use during the <i>in vivo</i> study.

<p>Use of the standard instrument is denoted by a grey background and use of the force-limiting instrument with a white background.</p

Participant demographics.

<p>Participant demographics.</p

Maximum forces exerted by: intermediates and experts using the standard instrument; novices using the standard instrument; and novices using the force-limiting instrument.

<p>Points represent outliers (star greater than 3 times the interquartile range).</p

<i>In vivo</i> performance of an intermediate surgeon using the standard versus force-limiting instrument.

<p><i>In vivo</i> performance of an intermediate surgeon using the standard versus force-limiting instrument.</p

Laboratory performance of: novice surgeons versus intermediate and expert surgeons using the standard instrument; and novice surgeons using the standard versus force-limiting instrument.

<p>Laboratory performance of: novice surgeons versus intermediate and expert surgeons using the standard instrument; and novice surgeons using the standard versus force-limiting instrument.</p