ABSTRACT
ObjectiveTo assess the COVID-19 incidence per economic activity during the Autumn wave 2020 in Belgium. MethodsThe 14-day incidence of confirmed COVID-19 cases per NACE-BEL code is described in the periods immediately preceding the Belgian more strict measures of October 19, 2020, and is evaluated longitudinally by a Gaussian-Gaussian modelling two-stage approach. Additionally, the number of high-risk contacts in working segments and regions is described. ResultsThe peak of COVID-19 14-day incidence in most NACE-BEL sectors is reached in the period October 20-November 2, 2020 and was considerably higher than average in human health activities, residential care activities, fitness facilities, human resource provision, hairdressing and other beauty treatment and some public service activities. Human health activities, residential care activities, food and beverage service activities, hotels, arts, food retail activities, and human resources provision have high pre-lockdown incidences. The frequency of index cases that report more than two high risk contacts is increasing over time in all sectors. ConclusionDespite the restrictive protocols present in many sectors before the Autumn wave, employees in activities where close contact with others is high, show increased risk of COVID-19 infection. Especially sports activities are among the highest risk activities. Finally, the increasing amount of high-risk contacts by COVID-19 confirmed cases is compatible with the decreasing motivation over time to adhere to the measures. Key Messages What is already known about this subject?Certain occupational sectors, such as human health and care, food and beverage, cultural and sport activities, have been related to a high risk of SARS-CoV-2 infection at the workplace. What are the new findings?COVID-19 confirmed cases of employees are linked with the main economic activity of their employer. The effect of opening of sectors, potentially under restrictive protocols, is evaluated. Despite the restrictive protocols present in many sectors, employees in activities where close and/or prolonged contact with others is high exhibit increased risk of COVID-19 infection, even higher than the high-risk sector of human health and care. Full restriction of these sectors decreases adequately the COVID-19 incidences, even in those sectors with physical contacts that remain open, for example human health, care and food shops. Finally, the increasing amount of high-risk contacts by COVID-19 confirmed cases might be related to decreasing motivation over time to adhere to the measures. How might this impact on policy or clinical practice in the foreseeable future?These insights offer guidance to policy makers on which economic activity to restrict or subject to stricter protocols to better control the COVID-19 pandemic whilst keeping the work floor as safe as possible.
ABSTRACT
SO_SCPLOWUMMARYC_SCPLOWThe Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, excess mortality has been suggested rather than reported COVID-19 deaths. Excess mortality, however, requires estimation of mortality under non-pandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the subject-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.
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.
