ABSTRACT
ObjectivesBurden of Disease frameworks facilitate estimation of the health impact of diseases to be translated into a single measure, such as the Disability-Adjusted-Life-Year (DALY). MethodsDALYs were calculated as the sum of Years of Life Lost (YLL) and Years Lived with Disability (YLD) directly associated with COVID-19 in the Republic of Ireland (RoI) from March 01, 2020, to February 28, 2021. Life expectancy is based on the Global Burden of Disease (GBD) Study life tables for 2019. ResultsThere were 220,273 confirmed cases with a total of 4,500 deaths as a direct result of COVID-19. DALYs were estimated to be 51,532.1 (95% Uncertainty Intervals [UI] 50,671.6, 52,294.3). Overall, YLL contributed to 98.7% of the DALYs. Of total symptomatic cases, 6.5% required hospitalisation and of those hospitalised 10.8% required intensive care unit treatment. COVID-19 was likely to be the second highest cause of death over our studys duration. ConclusionEstimating the burden of a disease at national level is useful for comparing its impact with other diseases in the population and across populations. This work sets out to standardise a COVID-19 BoD methodology framework for the RoI and comparable nations in the EU.
ABSTRACT
COVID-19 has affected all countries. Its containment represents a unique challenge for India due to a large population (>1.38 billion) across a wide range of population densities. Assessment of the COVID-19 disease burden is required to put the disease impact into context and support future pandemic policy development. Here, we present the national-level burden of COVID-19 in India in 2020 that accounts for differences across urban and rural regions and across age groups. Disability-adjusted life years (DALY) due to COVID-19 were estimated in the Indian population in 2020, comprised of years of life lost (YLL) and years lived with disability (YLD). Scenario analyses were conducted to account for excess deaths not recorded in the official data and for reported COVID-19 deaths. The direct impact of COVID-19 in 2020 in India was responsible for 14,106,060 (95% uncertainty interval [UI] 14,030,129-14,213,231) DALYs, consisting of 99.2% (95% UI 98.47-99.64%) YLLs and 0.80% (95% UI 0.36-1.53) YLDs. DALYs were higher in urban (56%; 95% UI 56-57%) than rural areas (44%; 95% UI 43.4-43.6) and in males (64%) than females (36%). In absolute terms, the highest DALYs occurred in the 51-60-year-old age group (28%) but the highest DALYs per 100,000 persons were estimated for the 71-80 year old age group (5,481; 95% UI 5,464-5,500 years). There were 4,823,791 (95% UI 4,760,908-4,924,307) DALYs after considering reported COVID-19 deaths only. The DALY estimations have direct and immediate implications not only for public policy in India, but also internationally given that India represents one sixth of the worlds population.
ABSTRACT
ObjectiveScrutiny of COVID-19 mortality in Belgium over the period 8 March - 9 May 2020 (Weeks 11-19), using number of deaths per million, infection fatality rates, and the relation between COVID-19 mortality and excess death rates. DataPublicly available COVID-19 mortality (2020); overall mortality (2009 - 2020) data in Belgium and demographic data on the Belgian population; data on the nursing home population; results of repeated sero-prevalence surveys in March-April 2020. Statistical methodsReweighing, missing-data handling, rate estimation, visualization. ResultsBelgium has virtually no discrepancy between COVID-19 reported mortality (confirmed and possible cases) and excess mortality. There is a sharp excess death peak over the study period; the total number of excess deaths makes April 2020 the deadliest month of April since WWII, with excess deaths far larger than in early 2017 or 2018, even though influenza-induced January 1951 and February 1960 number of excess deaths were similar in magnitude. Using various sero-prevalence estimates, infection fatality rates (IFRs; fraction of deaths among infected cases) are estimated at 0.38 - 0.73% for males and 0.20 - 0.39% for females in the non-nursing home population (non-NHP), and at 0.79 - 1.52% for males and 0.88 - 1.31% for females in the entire population. Estimates for the NHP range from 38 to 73% for males and over 22 to 37% for females. The IFRs rise from nearly 0% under 45 years, to 4.3% and 13.2% for males in the non-NHP and the general population, respectively, and to 1.5% and 11.1% for females in the non-NHP and general population, respectively. The IFR and number of deaths per million is strongly influenced by extensive reporting and the fact that 66.0% of the deaths concerned NH residents. At 764 (our re-estimation of the figure 735, presented by "Our World in Data"), the number of COVID-19 deaths per million led the international ranking on May 9, 2020, but drops to 262 in the non-NHP. The NHP is very specific: age-related increased risk; highly prevalent comorbidities that, while non-fatal in themselves, exacerbate COVID-19; larger collective households that share inadvertent vectors such as caregivers and favor clustered outbreaks; initial lack of protective equipment, etc. High-quality health care countries have a relatively older but also more frail population [1], which is likely to contribute to this result. Thumbnail summary: What this paper addsCOVID-19 mortality and its relation to excess deaths, case fatality rates (CFRs), infection fatality rates (IFRs), and number of deaths per million are constantly being reported for a large number of countries globally. This study adds detailed insight in the Belgian situation over the period 8 March - 9 May 2020 (Week 11-Week 19). Belgium has virtually no discrepancy between COVID-19 reported mortality (confirmed and possible cases) and excess mortality. This, combined with a high fraction of possible cases that is COVID-19 related [2] provides a basis for using all COVID-19 cases and thus not only the confirmed ones, in IFR estimation. Against each of the years from 2009 and 2019 and the average thereof, there is a strong excess death peak in 2020, which nearly entirely coincides with confirmed plus possible COVID-19 cases. The excess death/COVID-19 peak rises well above seasonal fluctuations seen in the first trimester during the most recent decade (induced in part by seasonal influenza). In the second week of April 2020, twice as many people died than in the corresponding week of the reference year. April 2020 was the deadliest month of April since WWII, although January 1951 and February 1960 saw similar figures. More recently, in the winter of 2017-2018, there was 4.6% excess mortality in Belgium (70,215 actual deaths; 3093 more than the Be-MOMO-model prediction). In the winter of 2016-2017, there was an excess of 3284 deaths (4.9% excess mortality) https://epistat.wiv-isp.be/docs/momo/Be-MOMO%20winter%202017-18%20report_FR.pdf. At 764 (our estimate), the number of COVID-19 deaths per million leads the international ranking, but drops sharply to 262 in the non-nursing home population. CFR is not a good basis for international comparison, except as a tool in estimating global infection fatality rates [2]. These authors used asymptotic models to derive IFR as a limit of CFR. CFR is strongly influenced by testing strategy, and in several studies the delay between case confirmation and deaths is not accounted for. The handling of possible cases is ambiguous at best. We do not consider it here. Bias and precision in estimation of IFR is influenced by difficulties surrounding the estimation of sero-prevalence, such as sensitivity and specificity of the tests used [3], time to IgM and in particular IgG seroconversion [4], and potential selection bias occurring in data from residual sample surveys. A sensitivity analysis is undertaken by augmenting one primary with three auxiliary estimates of sero-prevalence. Because in Belgium there is a very close agreement between excess mortality on the one hand and confirmed and possible COVID-19 cases combined on the other, and because an international study [2] suggested that a fraction as high as 0.9 of possible cases could be attributable to COVID-19 [5], it is a reasonable choice to use all COVID-19 cases in IFR estimation. This encompasses a large fraction of deaths occurring in nursing homes. The IFR values obtained align with international values [2]. Using various sero-prevalence estimates, IFRs across all ages are estimated at 0.38 - 0.73% for males and 0.20 - 0.39% for females in the non-nursing home population (non-NHP), and at 0.79 - 1.52% for males and 0.88 - 1.31% for females in the entire population. Estimates for the NHP range from 38 to 73% for males and over 22 to 37% for females. The IFRs rise from nearly 0% under 45 years, to 4.3% and 13.2% for males in the non-NHP and the general population, respectively, and to 1.5% and 11.1% for females in the non-NHP and general population, respectively. The IFR is strongly influenced by extensive death cases reporting and the fact that 66.0% of the deaths concerned NH residents. Apart from a strong age-related gradient, also for each age category, IFRs are substantially higher in males than in females Because of these dependencies, IFRs should be considered in an age, gender, and sub-population specific manner. The same proviso is made for the number of deaths per million. An important such population is the NHP because of a specific cocktail: age-related increased risk; highly prevalent comorbidities that, while non-fatal in themselves, exacerbate COVID-19; larger collective households that share inadvertent vectors such as caregivers; initial lack of protective equipment, etc. High-quality health care countries have a relatively older but also more frail population [1], which might contribute.
ABSTRACT
The prevalence and associated risk factors of Toxocara vitulorum infection in buffalo and cattle calves was studied in 3 provinces in central Cambodia. Fecal samples were collected from 517 calves between the age of 1-15 weeks and processed for nematode egg counts by a modified McMaster method. A total of 64 calves were found to excrete T. vitulorum eggs in their feces (12.4%; 95% exact CI: 9.7-15.5). The mean fecal egg count was 2,798 EPG (SD=16,351; range=0-224,400). A multivariable generalized linear mixed model showed higher odds of T. vitulorum infection for buffalo versus cattle, for animals aged 4-8 weeks versus younger and older ones, and for animals with strongyle infection. There was no association with fecal consistency. Farmers should be aware of the potential impact of T. vitulorum, and treat their calves at the age of 2-3 weeks with anthelmintics such as benzimidazoles or pyrantel.
