key: cord-0928847-63hs1loe
authors: Fisman, David; Greer, Amy; Tuite, Ashleigh
title: Standardization and Age-Distribution of COVID-19: Implications for Variability in Case Fatality and Outbreak Identification
date: 2020-04-14
journal: nan
DOI: 10.1101/2020.04.09.20059832
sha: 1edd2ed5ca36137c64554e553d7ba6b5bf3909ca
doc_id: 928847
cord_uid: 63hs1loe

Background: Epidemiological data from the COVID-19 pandemic has demonstrated variability in attack rates by age, and country-to-country variability in case fatality ratio (CFR). Objective: To use direct and indirect standardization for insights into the impact of age-specific under-reporting on between-country variability in CFR, and apparent size of COVID-19 epidemics. Design: Post-hoc secondary data analysis (case studies), and mathematical modeling. Setting: China, global. Interventions: None. Measurements: Data were extracted from a sentinel epidemiological study by the Chinese Center for Disease Control (CCDC) that describes attack rates and CFR for COVID-19 in China prior to February 12, 2020. Standardized morbidity ratios (SMR) were used to impute missing cases and adjust CFR. Age-specific attack rates and CFR were applied to different countries with differing age structures (Italy, Japan, Indonesia, and Egypt), in order to generate estimates for CFR, apparent epidemic size, and time to outbreak recognition for identical age-specific attack rates. Results: SMR demonstrated that 50-70% of cases were likely missed during the Chinese epidemic. Adjustment for under-recognition of younger cases decreased CFR from 2.4% to 0.8% (assuming 50% case ascertainment in older individuals). Standardizing the Chinese epidemic to countries with older populations (Italy, and Japan) resulted in larger apparent epidemic sizes, higher CFR and earlier outbreak recognition. The opposite effect was demonstrated for countries with younger populations (Indonesia, and Egypt). Limitations: Secondary data analysis based on a single country at an early stage of the COVID-19 pandemic, with no attempt to incorporate second order effects (ICU saturation) on CFR. Conclusion: Direct and indirect standardization are simple tools that provide key insights into between-country variation in the apparent size and severity of COVID-19 epidemics.

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is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101 //doi.org/10. /2020 individuals). Standardizing the Chinese epidemic to countries with older 48 populations (Italy, and Japan) resulted in larger apparent epidemic sizes, higher 49 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101 //doi.org/10. /2020 

We were struck by the absence of reported COVID-19 cases in younger 78 individuals in early reports from China. A pandemic disease is defined by the 79 novelty of the pathogen and absence of population-level immunity, such that all 80 age groups in a population should be equally susceptible to infection. Inasmuch 81 as more severe cases are more likely to be recognized, the under-recognition of 82 disease in younger individuals serves as a metric for differential disease severity 83 by age, and also provides important information that can be used to adjust case 84 fatality ratios for likely under-reporting. Furthermore, simple approaches to 85 quantify under-reporting can inform public health prevention strategies,

. CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101 //doi.org/10. /2020 5 because if unrecognized cases are extremely common, control methods that 87 focus on identification of cases, isolation and quarantine alone are likely to fail.

We sought to use simple epidemiological tools, such as direct and indirect 90 standardization (i.e., calculation of standardized morbidity ratios) to gain 91 insights into likely disease under-reporting and case fatality in mainland China.

We then applied these insights to infer likely differences in disease severity CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint (5). 2020 country population projections for China by age were obtained from 103 the United Nations using the UNWPP package in R (6, 7). While the Chinese 104 COVID-19 epidemic was centered on the province of Hubei, the epidemic rapidly 105 spread to involve all Chinese provinces. Therefore, we used the total Chinese 106 population data by age to calculate age-specific cumulative incidence over the 107 initial 9 weeks of the epidemic. We used these initial observations to perform all 108 subsequent analyses. CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 7

Given that COVID-19 is an emerging communicable disease and there is no pre-125 existing immunity in the population, attack rates should be similar across age 126 groups, or possibly even higher in children due to their more intense contact 127 structure (9). The elevated SMR in older age groups, combined with their higher 128 case fatality, is suggestive of increased case ascertainment in this group due to 129 greater clinical severity. Indeed, when active case finding has been performed 130 for pediatric cases, attack rates in younger groups have been similar to those in 131 the older age groups. We examined a series of "case studies" where incidence in 132 older individuals (age > 59) was assumed to be measured accurately, and 133 cumulative incidence in older individuals was then applied to younger age We evaluated the anticipated size, timing, and impact of an epidemic with 141 identical age-specific cumulative incidence and case fatality as observed in

China but applied to four countries outside of China. We standardized to 143 countries and areas with older age than China (Japan, Italy) and younger age 144 (Indonesia, Egypt) as a means of isolating the impact of age structure on 145 outbreak characteristics. While somewhat arbitrary, these regions have all 146 either been impacted by COVID-19 to some degree (Japan, and Italy) (10-12);

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Since China's large population size results in a far larger epidemic for a given 152 incidence, we used a ratio-of-ratio approach. The ratio of population in the 153 other, comparator country (P O ) to the Chinese population (P C ) was defined as R P 154 = P O /P C . The ratio of the observed epidemic size in the other, comparator 155 country (E 0 ) to observed Chinese epidemic size (E C ) was defined as R E = E O /E C .

The ratio of ratios was thus R E /R P , and can be interpreted as the relative 157 apparent outbreak size when an outbreak with identical age-specific attack 158 rates occurs in a population with an age-structure that differs from that of 

We modeled time to observation of deaths by modeling time to symptoms, 170 severe pneumonia, ICU admission, and death using parameter estimates 171 presented in Table 1 , assuming exponential failure time.

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is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.09.20059832 doi: medRxiv preprint 9 173

Based on data in (8), the crude cumulative incidence of observed COVID-19 in 176 mainland China up until February 11, 2020 was 3.1 per 100,000. By contrast, 177 cumulative incidence in those aged > 59 years was 5.6 per 100,000. Age-178 specific cumulative incidence and SMR by age are presented in Table 2 and 179 Supplementary Figure 1. It can be seen that SMR for age groups < 50 was 180 substantially lower than that in older age groups and most deaths were also 181 observed in older age groups ( Table 2) . When we assumed complete or near 182 complete ascertainment of cases in individuals aged >59, and adjusted 183 incidence in younger age groups accordingly, the adjusted CFR fell, and was 184 0.8% if we assume that only 50% of older cases were ascertained (Figure 1) .

Even if all cases were ascertained in older individuals, it was estimated that 186 46% of total cases were missed; if only 50% of older cases were ascertained it 187 was estimated that 75% of cases were missed (Figure 1 ).

When the Chinese epidemic was age-standardized using population pyramids 190 from other countries, standardization to younger populations (Indonesia, Egypt 191 and Iran) markedly reduced CFR, while adjustment to older countries or regions 192 (Japan, Italy) elevated CFR ( Table 3) . The ratio-of-ratios, R E /R P , was less than 1 193 for countries with younger populations, but greater than 1 for countries with 

The key driver of pandemic disease is a fully susceptible population; novel 226 pathogens have higher reproduction numbers when they first emerge but the 227 number drops once some proportion of the population has become immune 228 (18). This leads to very high attack rates early in a pandemic. Furthermore, 229 vulnerability to infection should be equally distributed across the population, 230 with incidence expected to be highest in children, who have the highest rates 231 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.09.20059832 doi: medRxiv preprint and intensity of person-to-person contact. As such, an absence of pediatric 232 cases in national reporting data represents an index of under-reporting rather 233 than immunity to infection and can be used as a means of quickly adjusting 234 models for under-reported fractions through simple, easily applied methods 235 such as direct and indirect standardization, which we employ here. Bayesian 236 methods provide a more computationally intensive and more technically 237 challenging approach to the same problem (2).

The extraordinary case-fatality in the COVID-19 pandemic (as high as 10-12% 

A key limitation of this work is that much of the work focusses on an epidemic 255 in a single country, at an early point in the COVID-19 pandemic. Indeed, the 256 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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Figure is a graphical representation of data presented in Table 2 . SMR are 299 estimated as 100 x observed incidence divided by expected incidence, which in 300 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101 //doi.org/10. /2020 15 the context of a pandemic is approximately equal in all age groups, or 301 somewhat higher in younger individuals.

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is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101 //doi.org/10. /2020 mortality ratios by region of residence, Israel, 1987 Israel, -1994 

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