Undiagnosed diseases: Needs and opportunities in 20 countries participating in the Undiagnosed Diseases Network International

Introduction: Rare diseases (RD) are a health priority worldwide, overall affecting hundreds of millions of people globally. Early and accurate diagnosis is essential to support clinical care but remains challenging in many countries, especially the low- and medium-income ones. Hence, undiagnosed RD (URD) account for a significant portion of the overall RD burden. Methods: In October 2020, the Developing Nations Working Group of the Undiagnosed Diseases Network International (DNWG-UDNI) launched a survey among its members, belonging to 20 countries across all continents, to map unmet needs and opportunities for patients with URD. The survey was based on questions with open answers and included eight different domains. Conflicting interpretations were resolved in contact with the partners involved. Results: All members responded to the survey. The results indicated that the scientific and medical centers make substantial efforts to respond to the unmet needs of patients. In most countries, there is a high awareness of RD issues. Scarcity of resources was highlighted as a major problem, leading to reduced availability of diagnostic expertise and research. Serious equity in accessibility to services were highlighted both within and between participating countries. Regulatory problems, including securing informed consent, difficulties in sending DNA to foreign laboratories, protection of intellectual property, and conflicts of interest on the part of service providers, remain issues of concern. Finally, most respondents stressed the need to strengthen international cooperation in terms of data sharing, clinical research, and diagnostic expertise for URD patients in low and medium income countries. Discussion: The survey highlighted that many countries experienced a discrepancy between the growing expertise and scientific value, the level of awareness and commitment on the part of relevant parties, and funding bodies. Country-tailored public health actions, including general syllabus of medical schools and of the education of other health professionals, are needed to reduce such gaps.VSh is supported by Health Systems Research Institute of Thailand (65-040). SJ is supported by National Medical Research Council, Singapore (Grants ID CSAINV21jun-0003 and CIRG22jul-0003).S

Antibody tests for identification of current and past infection with SARS-CoV-2

Background The diagnostic challenges associated with the COVID‐19 pandemic resulted in rapid development of diagnostic test methods for detecting SARS‐CoV‐2 infection. Serology tests to detect the presence of antibodies to SARS‐CoV‐2 enable detection of past infection and may detect cases of SARS‐CoV‐2 infection that were missed by earlier diagnostic tests. Understanding the diagnostic accuracy of serology tests for SARS‐CoV‐2 infection may enable development of effective diagnostic and management pathways, inform public health management decisions and understanding of SARS‐CoV‐2 epidemiology. Objectives To assess the accuracy of antibody tests, firstly, to determine if a person presenting in the community, or in primary or secondary care has current SARS‐CoV‐2 infection according to time after onset of infection and, secondly, to determine if a person has previously been infected with SARS‐CoV‐2. Sources of heterogeneity investigated included: timing of test, test method, SARS‐CoV‐2 antigen used, test brand, and reference standard for non‐SARS‐CoV‐2 cases. Search methods The COVID‐19 Open Access Project living evidence database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) was searched on 30 September 2020. We included additional publications from the Evidence for Policy and Practice Information and Co‐ordinating Centre (EPPI‐Centre) ‘COVID‐19: Living map of the evidence’ and the Norwegian Institute of Public Health ’NIPH systematic and living map on COVID‐19 evidence’. We did not apply language restrictions. Selection criteria We included test accuracy studies of any design that evaluated commercially produced serology tests, targeting IgG, IgM, IgA alone, or in combination. Studies must have provided data for sensitivity, that could be allocated to a predefined time period after onset of symptoms, or after a positive RT‐PCR test. Small studies with fewer than 25 SARS‐CoV‐2 infection cases were excluded. We included any reference standard to define the presence or absence of SARS‐CoV‐2 (including reverse transcription polymerase chain reaction tests (RT‐PCR), clinical diagnostic criteria, and pre‐pandemic samples). Data collection and analysis We use standard screening procedures with three reviewers. Quality assessment (using the QUADAS‐2 tool) and numeric study results were extracted independently by two people. Other study characteristics were extracted by one reviewer and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test and, for meta‐analysis, we fitted univariate random‐effects logistic regression models for sensitivity by eligible time period and for specificity by reference standard group. Heterogeneity was investigated by including indicator variables in the random‐effects logistic regression models. We tabulated results by test manufacturer and summarised results for tests that were evaluated in 200 or more samples and that met a modification of UK Medicines and Healthcare products Regulatory Agency (MHRA) target performance criteria. Main results We included 178 separate studies (described in 177 study reports, with 45 as pre‐prints) providing 527 test evaluations. The studies included 64,688 samples including 25,724 from people with confirmed SARS‐CoV‐2; most compared the accuracy of two or more assays (102/178, 57%). Participants with confirmed SARS‐CoV‐2 infection were most commonly hospital inpatients (78/178, 44%), and pre‐pandemic samples were used by 45% (81/178) to estimate specificity. Over two‐thirds of studies recruited participants based on known SARS‐CoV‐2 infection status (123/178, 69%). All studies were conducted prior to the introduction of SARS‐CoV‐2 vaccines and present data for naturally acquired antibody responses. Seventy‐nine percent (141/178) of studies reported sensitivity by week after symptom onset and 66% (117/178) for convalescent phase infection. Studies evaluated enzyme‐linked immunosorbent assays (ELISA) (165/527; 31%), chemiluminescent assays (CLIA) (167/527; 32%) or lateral flow assays (LFA) (188/527; 36%). Risk of bias was high because of participant selection (172, 97%); application and interpretation of the index test (35, 20%); weaknesses in the reference standard (38, 21%); and issues related to participant flow and timing (148, 82%). We judged that there were high concerns about the applicability of the evidence related to participants in 170 (96%) studies, and about the applicability of the reference standard in 162 (91%) studies. Average sensitivities for current SARS‐CoV‐2 infection increased by week after onset for all target antibodies. Average sensitivity for the combination of either IgG or IgM was 41.1% in week one (95% CI 38.1 to 44.2; 103 evaluations; 3881 samples, 1593 cases), 74.9% in week two (95% CI 72.4 to 77.3; 96 evaluations, 3948 samples, 2904 cases) and 88.0% by week three after onset of symptoms (95% CI 86.3 to 89.5; 103 evaluations, 2929 samples, 2571 cases). Average sensitivity during the convalescent phase of infection (up to a maximum of 100 days since onset of symptoms, where reported) was 89.8% for IgG (95% CI 88.5 to 90.9; 253 evaluations, 16,846 samples, 14,183 cases), 92.9% for IgG or IgM combined (95% CI 91.0 to 94.4; 108 evaluations, 3571 samples, 3206 cases) and 94.3% for total antibodies (95% CI 92.8 to 95.5; 58 evaluations, 7063 samples, 6652 cases). Average sensitivities for IgM alone followed a similar pattern but were of a lower test accuracy in every time slot. Average specificities were consistently high and precise, particularly for pre‐pandemic samples which provide the least biased estimates of specificity (ranging from 98.6% for IgM to 99.8% for total antibodies). Subgroup analyses suggested small differences in sensitivity and specificity by test technology however heterogeneity in study results, timing of sample collection, and smaller sample numbers in some groups made comparisons difficult. For IgG, CLIAs were the most sensitive (convalescent‐phase infection) and specific (pre‐pandemic samples) compared to both ELISAs and LFAs (P < 0.001 for differences across test methods). The antigen(s) used (whether from the Spike‐protein or nucleocapsid) appeared to have some effect on average sensitivity in the first weeks after onset but there was no clear evidence of an effect during convalescent‐phase infection. Investigations of test performance by brand showed considerable variation in sensitivity between tests, and in results between studies evaluating the same test. For tests that were evaluated in 200 or more samples, the lower bound of the 95% CI for sensitivity was 90% or more for only a small number of tests (IgG, n = 5; IgG or IgM, n = 1; total antibodies, n = 4). More test brands met the MHRA minimum criteria for specificity of 98% or above (IgG, n = 16; IgG or IgM, n = 5; total antibodies, n = 7). Seven assays met the specified criteria for both sensitivity and specificity. In a low‐prevalence (2%) setting, where antibody testing is used to diagnose COVID‐19 in people with symptoms but who have had a negative PCR test, we would anticipate that 1 (1 to 2) case would be missed and 8 (5 to 15) would be falsely positive in 1000 people undergoing IgG or IgM testing in week three after onset of SARS‐CoV‐2 infection. In a seroprevalence survey, where prevalence of prior infection is 50%, we would anticipate that 51 (46 to 58) cases would be missed and 6 (5 to 7) would be falsely positive in 1000 people having IgG tests during the convalescent phase (21 to 100 days post‐symptom onset or post‐positive PCR) of SARS‐CoV‐2 infection. Authors' conclusions Some antibody tests could be a useful diagnostic tool for those in whom molecular‐ or antigen‐based tests have failed to detect the SARS‐CoV‐2 virus, including in those with ongoing symptoms of acute infection (from week three onwards) or those presenting with post‐acute sequelae of COVID‐19. However, antibody tests have an increasing likelihood of detecting an immune response to infection as time since onset of infection progresses and have demonstrated adequate performance for detection of prior infection for sero‐epidemiological purposes. The applicability of results for detection of vaccination‐induced antibodies is uncertain

Unmet needs in countries participating in the undiagnosed diseases network international: an international survey considering national health care and economic indicators

BackgroundPatients, families, the healthcare system, and society as a whole are all significantly impacted by rare diseases (RDs). According to various classifications, there are currently up to 9,000 different rare diseases that have been recognized, and new diseases are discovered every month. Although very few people are affected by each uncommon disease individually, millions of people are thought to be impacted globally when all these conditions are considered. Therefore, RDs represent an important public health concern. Although crucial for clinical care, early and correct diagnosis is still difficult to achieve in many nations, especially those with low and middle incomes. Consequently, a sizeable amount of the overall burden of RD is attributable to undiagnosed RD (URD). Existing barriers and policy aspects impacting the care of patients with RD and URD remain to be investigated.MethodsTo identify unmet needs and opportunities for patients with URD, the Developing Nations Working Group of the Undiagnosed Diseases Network International (DNWG-UDNI) conducted a survey among its members, who were from 20 different nations. The survey used a mix of multiple choice and dedicated open questions covering a variety of topics. To explore reported needs and analyze them in relation to national healthcare economical aspects, publicly available data on (a) World Bank ranking; (b) Current health expenditure per capita; (c) GDP per capita; (d) Domestic general government health expenditure (% of GDP); and (e) Life expectancy at birth, total (years) were incorporated in our study.ResultsThis study provides an in-depth evaluation of the unmet needs for 20 countries: low-income (3), middle-income (10), and high-income (7). When analyzing reported unmet needs, almost all countries (N = 19) indicated that major barriers still exist when attempting to improve the care of patients with UR and/or URD; most countries report unmet needs related to the availability of specialized care and dedicated facilities. However, while the countries ranked as low income by the World Bank showed the highest prevalence of referred unmet needs across the different domains, no specific trend appeared when comparing the high, upper, and low-middle income nations. No overt trend was observed when separating countries by current health expenditure per capita, GDP per capita, domestic general government health expenditure (% of GDP) and life expectancy at birth, total (years). Conversely, both the GDP and domestic general government health expenditure for each country impacted the presence of ongoing research.ConclusionWe found that policy characteristics varied greatly with the type of health system and country. No overall pattern in terms of referral for unmet needs when separating countries by main economic or health indicators were observed. Our findings highlight the importance of identifying actionable points (e.g., implemented orphan drug acts or registries where not available) in order to improve the care and diagnosis of RDs and URDs on a global scale

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation

Very-Long-Chain Acyl-Co-Enzyme A Dehydrogenase Deficiency Presenting as Rhabdomyolysis: First Case Report from Sri Lanka

Background. Rhabdomyolysis can be either inherited or acquired such as in metabolic myopathies. Very-long-chain acyl-CoA dehydrogenase deficiency is a rare fatty acid oxidation disorder which presents with different phenotypes, and the mild adult form can present as intermittent rhabdomyolysis. Here, we present the first adult case of very-long-chain acyl-CoA dehydrogenase deficiency presenting as rhabdomyolysis in a Sri Lankan patient. Case Presentation. A 36-year-old Sri Lankan man who was born to consanguineous parents presented with severe generalized muscle pain, stiffness, and dark-coloured urine for three days following prolonged low-intensity activity. Since fourteen years of age, he has had multiple similar episodes, where one episode was complicated with acute kidney injury. His eldest brother also suffered from the similar episode. Examination revealed only generalized muscle tenderness without any weakness. His creatine phosphokinase level was above 50,000 IU/L, and he had myoglobinuria. Molecular genetic tests confirmed the diagnosis of very-long-chain acyl-CoA dehydrogenase deficiency. Following a successful recovery devoid of complications, he remained asymptomatic with lifestyle adjustments. Conclusion. Very-long-chain acyl-CoA dehydrogenase deficiency is a rare inherited cause of metabolic myopathy that gives rise to intermittent rhabdomyolysis in adults. Prompt diagnosis is essential to prevent complications and prevent its recurrence

Symptomatology of COVID-19 - Lessons from a meta-analysis across 13 countries

Background: COVID-19 pandemic has resulted in varying clinical manifestations and mortality rates. There is no consensus on the symptomatology that would guide researchers and clinicians. Aims and Objectives: The objective was to identify symptoms and their frequencies of COVID-19 with a meta-analysis of studies from several countries. Materials and Methods: Data sources: A systematic review using PubMed and Google Scholar data sources and reference tracing were used to identify 7176 articles. Eligibility criteria: Suitable articles were selected manually with selection criteria and 14 original articles included in meta-analysis. Data abstraction and analysis: PRISMA guidelines used for data abstraction and a table was generated by feeding it with numbers and proportions of each symptom described. A meta-analysis was carried out using random effect models on each symptom separately across the studies and their prevalence rates and 95% confident intervals were calculated. Results: Selected 14 studies, either cross-sectional or cohort studies are analyzed. There were 2,660 confirmed cases of COVID-19. The majority were from China (n=2,439, 91.7%) and remainder from the Netherlands, Italy, Korea, and India and one article from Europe. There were a total of 32 symptoms identified from the meta-analysis and additional 7 symptoms were identified from reference searching. The most common symptoms were (prevalence >50%): fever (79.56%, 95% CI: 72.17–86.09%), malaise (63.3%, 95% CI: 53.1–73.0%), cough (56.7%, 95% CI: 48.6–64.6%), and cold (55.6%, 95% CI: 45.2–65.7%). Symptoms of intermediate incidence (5–49%) were anosmia, sneezing, ocular pain, fatigue, sputum production, arthralgia, tachypnea, palpitation, headache, chest tightness, shortness of breath, chills, myalgia, sore throat, anorexia, weakness, diarrhea, rhinorrhea, dizziness, nausea, altered level of consciousness, vomiting, and abdominal pain. Rare symptoms (<5%): tonsil swelling, hemoptysis, conjunctival injection, lymphadenopathy, and rash. Conclusion: We found (25/32, from meta-analysis) symptoms to be present in ≥5% of cases which could be considered as “typical” symptoms of COVID-19. The list of symptoms we identified is different from those documents released by the WHO, CDC, NHS, Chinese CDC, Institute Pasteur and Mayo Clinic. The compiled list would be useful for future researchers to document a comprehensive picture of the illness

Symptomatology of Coronavirus Disease 2019 (COVID-19):Lessons from A Meta-Analysis Across 13 Countries

Background: COVID-19 pandemic has resulted in varying clinical manifestations and mortality rates. There is no consensus on the symptomatology that would guide researchers and clinicians.Objective: The objective of the study was to identify symptoms and their frequencies of coronavirus disease 2019 with a meta-analysis of studies from several countries. Data sources: A systematic review using PubMed and Google Scholar data sources and reference tracing were used to identify 7176 relevant articles. Eligibility criteria: Suitable articles were selected manually with selection criteria and 14 original articles included for meta-analysis. Data abstraction analysis: PRISMA guideline was used for abstracting data. Then a table was generated by feeding it with numbers and proportions of each symptom described in original studies. A meta-analysis was carried out using random effect models on each symptom separately across the studies and their prevalence rates and 95% confident intervals calculated.Results: We identified 14 relevant scientific papers, either cross-sectional or cohort studies and analyzed. There were 2,660 cases of COVID-19. he majority were from China (n=2,439, 91.7%) and remainder from the Netherlands, Italy, Korea and India and one article from Europe. There was a total of 32 symptoms (i.e. present in &gt;50% of patients): fever (79.56%, 95% CI: 72.17-86.09%), malaise (63.3%, 95% CI: 53.1 – 73.0%), cough (56.7. %, 95% CI: 48.6 - 64.6 %) and cold (55.6%, 95% CI: 45.2 – 65.7%). Symptoms of intermediate incidence (5-49%) were; anosmia, sneezing, ocular pain, fatigue, sputum production, arthralgia, tachypnea, palpitation, headache, chest tightness, shortness of breath, chills, myalgia, sore throat, anorexia, weakness, diarrhea, rhinorrhea, dizziness, nausea, altered level of consciousness, vomiting and abdominal pain. Rare symptoms (&lt;5% of patients) were: tonsil swelling, haemoptysis, conjunctival injection, lymphadenopathy and rash were uncommon symptoms of coronavirus disease (&lt;5%).Conclusion and implications of key findings: We found (25/32) symptoms to be present in =&gt;5% of cases which could be considered as “typical” symptoms of COVID-19. The list of symptoms we identified are different from those documents released by the WHO, CDC, NHS, Chinese CDC, Institute Pasteur and Mayo Clinic. The compiled list would be useful for future researchers to document a comprehensive picture of the illness

Ethical Responses to the COVID-19 Pandemic:Lessons from Sri Lanka

The COVID-19 pandemic has undoubtedly become an era-defining challenge for the entire world. It has implications not only in the public health sector but also in the global economy and political landscape. The prevention strategy that has been followed in Sri Lanka is unique. Early action taken by the government and the ministry of health, being one of pre-emptive quarantining and isolation of suspected contacts even before they developed symptoms, was vital to contain the spread of the disease. During the early phase, a nationwide lockdown in the form of a curfew was imposed which helped mitigate the spread of the virus. However, due to several lapses, there was a threat of community transmission; this was swiftly brought under control through ongoing government interventions. Thus, strict social/physical distancing measures enforced by the government, together with an increase in testing capacity, prevented widespread community transmission. Strictly containing the outbreaks as and when they were identified made it easier to bring the spread under control through contact tracing. In this article, we give an account of the strategy taken by Sri Lanka to mitigate the pandemic and comment on the lessons learned concerning the ethical responses to the COVID-19 crisis

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BackgroundEstimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.Methods22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.FindingsGlobal all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.InterpretationGlobal adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic