key: cord-0683969-67wvavyx
authors: Ergönül, Önder; Akyol, Merve; Tanrıöver, Cem; Tiemeier, Henning; Petersen, Eskild; Petrosillo, Nicola; Gönen, Mehmet
title: National case fatality rates of the COVID-19 pandemic
date: 2020-09-23
journal: Clin Microbiol Infect
DOI: 10.1016/j.cmi.2020.09.024
sha: 9e9e379cb9c66872993a4186b062ca306f908ef4
doc_id: 683969
cord_uid: 67wvavyx

OBJECTIVES: The case fatality rate (CFR) of Coronavirus disease 2019 (COVID-19) varies significantly between countries. We aimed to describe the associations of health indicators with the national CFRs of COVID-19. METHODS: We identified health indicators for each country potentially associated with the national CFRs of COVID-19. We extracted data for 18 variables from international administrative data sources for 34 member countries of the Organization for Economic Co-operation and Development (OECD). We excluded the collinear variables and examined the 16 variables in multivariable analysis. A dynamic web-based model was developed to analyse and display the associations for the CFRs of COVID-19. We followed the Guideline for Accurate and Transparent Health Estimates Reporting (GATHER). RESULTS: In multivariable analysis, the variables significantly associated with the increased CFRs were percent of obesity in ages >18 years (β = 3.26, 95% CI = [1.20, 5.33], p = 0.003), tuberculosis incidence (β = 3.15, 95% CI = [1.09, 5.22], p = 0.004), duration (days) since first death due to COVID-19 (β = 2.89, 95% CI = [0.83, 4.96], p = 0.008), median age (β = 2.83, 95% CI = [0.76, 4.89], p = 0.009). The COVID-19 test rate (β = -3.54, 95% CI = [-5.60, -1.47], p = 0.002), hospital bed density (β = -2.47, 95% CI = [-4.54, -0.41], p = 0.021), and rural population ratio (β = -2.19, 95% CI = [-4.25, -0.13], p = 0.039) decreased the CFR. CONCLUSIONS: The pandemic hits the population dense cities. Available hospital beds should be increased. Test capacity should be increased to enable more effective diagnostic tests. Older patients, and patients with obesity, and their caregivers should be warned about a potentially increased risk.

A novel coronavirus, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), 49 emerged in Wuhan, the capital of Hubei province in China in December 2019 [1] . The virus is 50 responsible for COVID-19, a disease ranging from a mild respiratory illness to a serious 51 condition that can cause considerable mortality [1, 2] . Unlike Severe Acute Respiratory 52 Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), which had case fatality 53 rates (CFR) of 9.5% and 34.4%, respectively; COVID-19 has a much lower CFR [3] . 54

However, SARS-CoV-2 is more transmissible with Ro of 3, on average, and caused a higher 55 total number of deaths [3] [4] [5] . 56 57 According to descriptive data sources, CFR of COVID-19 shows great variation between the 58 countries. Analysis of this variation might shed light on the bottlenecks of the pandemics. The 59 models that can project mortality based on health indicators and covariates could be beneficial 60 for the development of preventive measures. 61 62 We aimed to investigate the potential health indicators and covariates influencing national 63

We mainly used the OECD, WB, and WHO databases, which provided easily accessible, 116 reproducible, and standardized data. If necessary data were not available, we used additional 117 international sources for all countries. Among the initial 18 variables, 16 were obtained 118 directly from the main sources without any modification. We calculated cancer prevalence 119 and test rate by dividing the total numbers of all cancers and tests, respectively, by the total 120 population of that country. WHO We first checked the correlation between variables to screen for collinear variables (Figure 2 ). 127

We started with 18 variables and excluded three of them having Pearson's correlation greater 128 than 0.75 with at least one other variable from further analysis. We considered the number of 129 days after the first recorded death due to COVID-19 to represent the stage of the pandemic in 130 each country. Thus, we added this information as an adjustment variable (duration since the 131 first death) to take differences between countries in terms of their pandemic durations into 132 account, leading to 16 variables in total. We performed linear regression for each variable 133 adjusted by duration since the first death with national CFR values as the outcome. We then 134 performed multivariable linear regression with backward selection to identify a minimal set of 135 predictors from our variable list. In both adjusted and multivariable linear regression analyses,

In total, 18 variables representing risk factors (n = 11), health system indicators (n = 4) and 140

propensity covariates (n = 3) were analysed for 34 OECD-member countries ( Figure 1 ). After 141 collinearity check, the number of variables was reduced to 15. We decided not to include 142 three variables, namely, cancer incidence in ages 15+ (per 1000 people), population above age We included "duration since first death" variable to adjust our model since the pandemic 152 started at different times in different countries. We first analysed each 15 variable adjusted by 153 duration since first death by linear regression model ( Nation-wide distribution of the CFR and its significant predictors are presented in Figure 3 . In this study, we investigated the potential predictors of the national CFRs of COVID-19 in 165 34 OECD-member countries. Increased percent of obesity in ages >18 years, higher 166 tuberculosis incidence, longer duration since first death due to COVID-19, and older age were 167 found to be significantly associated with higher CFR, whereas increased COVID-19 test rate, 168 higher hospital bed density , and higher rural population were found to be significantly 169 associated with lower CFR in SARS-CoV-2 pandemic, by 18 August 2020 (Table 2) . We 170 developed a web-based tool to continuously update the calculations about the ongoing 171 pandemic that can be accessed at http://midas.ku.edu.tr/COVID19CFR. 172 173 Our findings suggest that obesity is significantly associated with higher CFR. Obesity is a 174 main risk factor for the comorbidities such as hypertension, diabetes mellitus and 175 cardiovascular diseases and is also linked to an increased risk of pneumonia [25] . In a study 176 from New York, hypertension and obesity were reported as the most common comorbidities 177 among the hospitalized COVID-19 patients [26] . In multivariable analysis, we detected that 178 increased hypertension was associated with higher CFR, although statistically not significant. patients requiring such long periods of hospital stay places pressure on the healthcare system. 210

As there are currently no treatment options available for COVID-19, and the treatment is 211 mainly supportive, it is of utmost importance that the patients have hospital access to benefit 212 from the supportive treatment, which could be life-saving. As an indicator of the capacity of 213 the healthcare system in pandemic, the intensive care unit (ICU) bed density could have been 214 studied, however standardized data for ICU bed density were not available for all 34 215 countries. 216

We found that the increasing percentage of rural population has a decreasing effect on the 218 national CFR. This could be directly related to the population density, which is much lower in 219 rural areas compared to the crowded cities that are the epicentres of the pandemic [35] . Our 220 finding supports the significance of the social distancing in control of the pandemic. 221

We found that increased tuberculosis incidence was significantly associated with CFR. In 223 countries where tuberculosis is highly prevalent there might be other risk factors for mortality. 224 There are several limitations of this study. We had to limit our study with 34 OECD member 229 countries because of the lack of the availability of data in many other countries. If it would be 230 possible, inclusion of resource poor and highly populated countries might highlight the impact 231 of economic variables on CFR. We extracted our data from the most relevant and reliable 232 databases, but not all the variables were effectively updated. Some variables could not be 233 added into the study as they did not have data for all countries. We reviewed risk factors such 234 as ischemic heart disease, heart failure, chronic kidney disease, chronic obstructive pulmonary 235 disease and asthma, but we could not include them into our study because they either did not 

[15] OECD, Hospital beds Total, Per 1000 inhabitants, 2018 or latest available, Organisation for 300 Economic Co-operation and Development, 2020. https://data.oecd.org/healtheqt/hospital-beds.htm.

(Accessed March 25, 2020).

[16] WB, Nurses and midwives (per 1,000 people), World Bank, 2020. 

32] WHO, Laboratory testing strategy recommendations for COVID-19, World Health Organization

Geographic distribution of hospital beds throughout China: a county-level 352 econometric analysis

Hospital Capacity and Operations in the Coronavirus 354 Disease 2019 (COVID-19) Pandemic -Planning for the Nth Patient

How Demographic Changes Make Us More Vulnerable to Pandemics