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Global, regional, and national burden of upper respiratory infections and otitis media, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021
Background
Upper respiratory infections (URIs) are the leading cause of acute disease incidence worldwide and contribute to a substantial health-care burden. Although acute otitis media is a common complication of URIs, the combined global burden of URIs and otitis media has not been studied comprehensively. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to explore the fatal and non-fatal burden of the two diseases across all age groups, including a granular analysis of children younger than 5 years, in 204 countries and territories from 1990 to 2021.
Methods
Mortality due to URIs and otitis media was estimated with use of vital registration and sample-based vital registration data, which are used as inputs to the Cause of Death Ensemble model to separately model URIs and otitis media mortality by age and sex. Morbidity was modelled with a Bayesian meta-regression tool using data from published studies identified via systematic reviews, population-based survey data, and cause-specific URI and otitis media mortality estimates. Additionally, we assessed and compared the burden of otitis media as it relates to URIs and examined the collective burden and contributing risk factors of both diseases.
Findings
The global number of new episodes of URIs was 12·8 billion (95% uncertainty interval 11·4 to 14·5) for all ages across males and females in 2021. The global all-age incidence rate of URIs decreased by 10·1% (–12·0 to –8·1) from 1990 to 2019. From 2019 to 2021, the global all-age incidence rate fell by 0·5% (–0·8 to –0·1). Globally, the incidence rate of URIs was 162 484·8 per 100 000 population (144 834·0 to 183 289·4) in 2021, a decrease of 10·5% (–12·4 to –8·4) from 1990, when the incidence rate was 181 552·5 per 100 000 population (160 827·4 to 206 214·7). The highest incidence rates of URIs were seen in children younger than 2 years in 2021, and the largest number of episodes was in children aged 5–9 years. The number of new episodes of otitis media globally for all ages was 391 million (292 to 525) in 2021. The global incidence rate of otitis media was 4958·9 per 100 000 (3705·4 to 6658·6) in 2021, a decrease of 16·3% (–18·1 to –14·0) from 1990, when the incidence rate was 5925·5 per 100 000 (4371·8 to 8097·9). The incidence rate of otitis media in 2021 was highest in children younger than 2 years, and the largest number of episodes was in children aged 2–4 years. The mortality rate of URIs in 2021 was 0·2 per 100 000 (0·1 to 0·5), a decrease of 64·2% (–84·6 to –43·4) from 1990, when the mortality rate was 0·7 per 100 000 (0·2 to 1·1). In both 1990 and 2021, the mortality rate of otitis media was less than 0·1 per 100 000. Together, the combined burden accounted for by URIs and otitis media in 2021 was 6·86 million (4·24 to 10·4) years lived with disability and 8·16 million (4·99 to 12·0) disability-adjusted life-years (DALYs) for all ages across males and females. Globally, the all-age DALY rate of URIs and otitis media combined in 2021 was 103 per 100 000 (63 to 152). Infants aged 1–5 months had the highest combined DALY rate in 2021 (647 per 100 000 [189 to 1412]), followed by early neonates (aged 0–6 days; 582 per 100 000 [176 to 1297]) and late neonates (aged 7–24 days; 482 per 100 000 [161 to 1052]).
Interpretation
The findings of this study highlight the widespread burden posed by URIs and otitis media across all age groups and both sexes. There is a continued need for surveillance, prevention, and management to better understand and reduce the burden associated with URIs and otitis media, and research is needed to assess their impacts on individuals, communities, economies, and health-care systems worldwide.
Funding
Bill & Melinda Gates Foundation
Comparative Study on Physicochemical and Biological Parameters of Water among Fish Culture and Reconstructed Pond at Jahangirnagar University Campus, Bangladesh
The study was conducted to investigate the physicochemical and biological parameters of fish culture and reconstructed pond at Jahangirnagar university campus. The physicochemical parameters of water in culture and reconstructed pond were analyzed during February to September, 2014 and the mean value of temperature were 30.21±0.89 ºC and 29.96±0.91 ºC, pH value were 7.20±0.29 and 6.97±0.39, Dissolve Oxygen (DO) value were 6.44±0.40mg-1 and 6.22±0.30mg-1, Biochemical Oxygen Demand (BOD5) value were 1.02±0.32mg-1 and 0.78±0.18mg-1, Total Dissolve Solid (TDS) were 0.69±0.04mg-1 and 0.64±0.04mg-1, Electric Conductivity (EC) value were 215.38±21.27?Scm-1 and 128.58±1.10?Scm-1. From the study of biological parameter, it was found that Chlorophyceae and Euglenophyceae were dominant in studied ponds and the abundance of phytoplankton are in the order of Chlorophyceae >Euglenophyceae > Bacillariophyceae > Cyanophyceae. The highest productivity was found in culture pond which indicates the suitability of using for aquaculture.J. Environ. Sci. & Natural Resources, 9(1): 1-7 2016</jats:p
Exploring impacts and livelihood vulnerability of riverbank erosion hazard among rural household along the river Padma of Bangladesh
Water, sanitation, hygiene and waste disposal practices as COVID-19 response strategy: insights from Bangladesh
An assessment of contamination and ecological risk of metals in sediments of the Guaracara, Caparo and Couva rivers in Trinidad, West Indies
Adaptive differential evolution based feature selection and parameter optimization for advised SVM classifier
© Springer International Publishing Switzerland 2015. This paper proposes a pattern recognition model for classification. Adaptive differential evolution based feature selection is used for dimensionality reduction and a new advised version of support vector machine is used for evaluation of selected features and for the classification. The tuning of the control parameters for differential evolution algorithm, parameter value optimization for support vector machine and selection of most relevant features form the datasets all are done together. This helps in dealing with their interdependent effect on the overall performance of the learning model. The proposed model is tested on some latest machine learning medical datasets and compared with some well-developed methods in literature. The proposed model provided quite convincing results on all the test datasets
