Global, regional, and national incidence, prevalence, and mortality of HIV, 1980–2017, and forecasts to 2030, for 195 countries and territories: a systematic analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017

Background Understanding the patterns of HIV/AIDS epidemics is crucial to tracking and monitoring the progress of prevention and control efforts in countries. We provide a comprehensive assessment of the levels and trends of HIV/AIDS incidence, prevalence, mortality, and coverage of antiretroviral therapy (ART) for 1980–2017 and forecast these estimates to 2030 for 195 countries and territories. Methods We determined a modelling strategy for each country on the basis of the availability and quality of data. For countries and territories with data from population-based seroprevalence surveys or antenatal care clinics, we estimated prevalence and incidence using an open-source version of the Estimation and Projection Package—a natural history model originally developed by the UNAIDS Reference Group on Estimates, Modelling, and Projections. For countries with cause-specific vital registration data, we corrected data for garbage coding (ie, deaths coded to an intermediate, immediate, or poorly defined cause) and HIV misclassification. We developed a process of cohort incidence bias adjustment to use information on survival and deaths recorded in vital registration to back-calculate HIV incidence. For countries without any representative data on HIV, we produced incidence estimates by pulling information from observed bias in the geographical region. We used a re-coded version of the Spectrum model (a cohort component model that uses rates of disease progression and HIV mortality on and off ART) to produce age-sex-specific incidence, prevalence, and mortality, and treatment coverage results for all countries, and forecast these measures to 2030 using Spectrum with inputs that were extended on the basis of past trends in treatment scale-up and new infections. Findings Global HIV mortality peaked in 2006 with 1·95 million deaths (95% uncertainty interval 1·87–2·04) and has since decreased to 0·95 million deaths (0·91–1·01) in 2017. New cases of HIV globally peaked in 1999 (3·16 million, 2·79–3·67) and since then have gradually decreased to 1·94 million (1·63–2·29) in 2017. These trends, along with ART scale-up, have globally resulted in increased prevalence, with 36·8 million (34·8–39·2) people living with HIV in 2017. Prevalence of HIV was highest in southern sub-Saharan Africa in 2017, and countries in the region had ART coverage ranging from 65·7% in Lesotho to 85·7% in eSwatini. Our forecasts showed that 54 countries will meet the UNAIDS target of 81% ART coverage by 2020 and 12 countries are on track to meet 90% ART coverage by 2030. Forecasted results estimate that few countries will meet the UNAIDS 2020 and 2030 mortality and incidence targets. Interpretation Despite progress in reducing HIV-related mortality over the past decade, slow decreases in incidence, combined with the current context of stagnated funding for related interventions, mean that many countries are not on track to reach the 2020 and 2030 global targets for reduction in incidence and mortality. With a growing population of people living with HIV, it will continue to be a major threat to public health for years to come. The pace of progress needs to be hastened by continuing to expand access to ART and increasing investments in proven HIV prevention initiatives that can be scaled up to have population-level impact

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Testing of the applicability of European diatom indices in the tropical rift valley lake, Lake Hawassa, in Ethiopia

Quantitative ecological monitoring of African lakes is needed to understand growing human pressures on ecosystems. Diatom-based indices are routinely used for this purpose elsewhere in the world, but have not yet been produced for the flora of African freshwater lakes. Here we tested the applicability of the European diatom indices on the biomonitoring system of Lake Hawassa, Ethiopia. Physico-chemical and benthic diatom sampling was done at nine sites with different degrees of human disturbance along the lakeshore area from February to November 2015 and 2016. A percentage disturbance score (PDS) was calculated at each site and categorised from no evident disturbance (0–25%) to high disturbance (75–100%). Based on this criterion and selected physico-chemical parameters, the sampling sites categorized into minimal, moderate and high disturbance. Seventeen diatom indices were calculated using Omnidia software version 5.3. Out of a total of 17 indices that were calculated using the Omnidia software, six were selected as potential metrics. The diatom indices had a high discrimination efficiency and were significantly correlated with most the environmental parameters (r > 0.6; p < 0.05). Among these, the trophic diatom index (TDI) and generic diatom index (IDG) showed the best potential to discriminate the three clustered sites, based on their ecological classification. Accordingly, although robust locally based indices are needed, the TDI and IDG diatom indices could be used in monitoring of water quality in tropical African rift lakes

Health-Related Quality of Life Among Type 2 Diabetes Mellitus Patients Using the 36-Item Short Form Health Survey (SF-36) in Central Ethiopia: A Multicenter Study

Habtamu Esubalew,1 Ayele Belachew,2 Yimer Seid,2 Habtamu Wondmagegn,3 Kidus Temesgen,1 Tsegazeab Ayele3 1School of Public Health, College of Medicine and Health Sciences, Arba Minch University, Arba Minch, Ethiopia; 2School of Public Health, College of Health Science, Addis Ababa University, Addis Ababa, Ethiopia; 3Department of Human Anatomy, College of Medicine and Health Sciences, Arba Minch University, Arba Minch, EthiopiaCorrespondence: Habtamu Esubalew, School of Public Health, College of Medicine and Health Sciences, Arba Minch University, P.O. Box 21, Arba Minch, Ethiopia, Tel +251 931547322, Email [email protected]: Diabetes, one of the major global health emergencies of the 21st century, can affect a patient’s quality of life. A compromised quality of life has adverse effects on self-care practices, resulting in inadequate glycemic control and an increased susceptibility to complications. In Ethiopia, there is a paucity of information regarding the quality of life of patients with type 2 diabetes mellitus. Therefore, this study aimed to assess health-related quality of life in type 2 diabetes mellitus patients.Methods: A cross-sectional study was conducted among type 2 diabetes mellitus patients attending diabetes follow-up clinics in selected public hospitals in Addis Ababa using short form- 36 (SF-36) health survey measures. Descriptive statistics were used to summarize the characteristics of the study participants. Simple and multiple linear regressions were done to identify significantly associated factors with health-related quality of life.Result: A total of 309 patients participated in this study. The mean scores of the physical and mental component summaries were 40.15 (SD = 7.27) and 48.11 (SD = 8.87), respectively. Being old, being overweight or obese, living with type 2 diabetes mellitus for more than fifteen years, taking combined medication, having diabetes-related complications, and having comorbidities were factors that reduced the mean score of the physical component summary (p-value< 0.05). Being obese and diabetes related complication were factors that negatively affect mental component summary (p-value < 0.05). On the other hand, being married was factors that positively affect mental component summary (p-value < 0.05).Conclusion: Older age, being married, obesity, overweight, longer duration of diabetes, using combined medications, diabetic-related complications, and co-morbidities were factors associated with health-related quality of life. Healthcare providers should strengthen counseling patients on lifestyle modifications such as diet modifications, and weight reduction.Keywords: health-related quality of life, type 2 diabetes mellitus, short-form-36 health surve

Dynamic electrical properties of polymer-carbon nanotube composites: Enhancement through covalent bonding

Composite formation between carbon nanotubes and polymers can dramatically enhance the electrical and thermal properties of the combined materials. We have prepared a composite from polystyrene and multi-walled carbon nanotubes (MWCNT) and, unlike traditional techniques of composite formation, we chose to polymerize styrene from the surface of dithiocarboxylic ester-functionalized MWCNTs to fabricate a unique composite material, a new technique dubbed "gRAFT" polymerization. The thermal stability of the polymer matrix in the covalently linked MWCNT-polystyrene composite is significantly enhanced, as demonstrated by a 15 degrees C increase of the decomposition temperature than that of the noncovalently linked MWCNT-polystyrene blend. Thin films made from the composite with low MWCNT loadings (< 0.9 wt%) are optically transparent, and we see no evidence of aggregation of nanotubes in the thin film or solution. The result from the conductivity measurement as a function of MWCNT loadings suggests two charge transport mechanisms: charge hopping in low MWCNT loadings (0.02-0.6 wt%) and ballistic quantum conduction in high loadings (0.6-0.9 wt%). The composite exhibits dramatically enhanced conductivity up to 33 S m(-1) at a low MWCNT loading (0.9 wt%)