key: cord-0809635-6coxz3l8 authors: SOURIS, M.; Gonzalez, J.-P. title: COVID-19: Spatial Analysis of Hospital Case-Fatality Rate in France date: 2020-05-20 journal: nan DOI: 10.1101/2020.05.16.20104026 sha: a951dae1eb7f0019ae5dc778fa587c40e7518eef doc_id: 809635 cord_uid: 6coxz3l8 When the population risk factors and reporting systems are similar, the assessment of the case-fatality (or lethality) rate (ratio of cases to deaths) represents a perfect tool for analyzing, understanding and improving the overall efficiency of the health system. The objective of this article is to estimate the influence of the hospital care system on lethality in metropolitan France during the inception of the COVID-19 epidemic, by analyzing the spatial variability of the hospital case-fatality rate between French districts. The results show that the higher case-fatality rates observed in certain districts are mostly related to the level of morbidity in the district, therefore to the overwhelming of the healthcare systems during the acute phases of the epidemic. However, the magnitude of this increase of case-fatality rate represents less than 10 per cent of the average case-fatality rate and cannot explain the magnitude of the variations in case-fatality rate reported by country by international organizations or information sites. These differences can only be explained by the systems for reporting cases and deaths, which, indeed, vary greatly from country to country, and not attributed to the care or treatment of patients, even during hospital stress due to epidemic peaks. Since the beginning of the epidemic, the case-fatality rate of COVID-19 and the 34 differences between countries have been the subject of many questions about national 35 pandemic response policies and patient treatment. Most studies on the lethality of 36 The case-fatality rate (or lethality rate) is the ratio between the number of closed 39 cases (i.e. recovered or dead) and the number of deaths due to the disease, it is 40 estimated by the healthcare system based on the reporting of these two values. The 41 case-fatality rate should not be confused with the mortality rate, which is the ratio of 42 the number of deaths to the total population, or also with the morbidity rate, which is 43 the ratio of the number of cases to the total population. Mortality and morbidity rates 44 depend on the extent of disease in a population, unlike case-fatality rates, which are 45 normally calculated independently of the number of infected persons [POR 08]. 46 The case-fatality rate of a disease in a population is an index of severity of the 47 disease in that population, and of the capacity of the healthcare system to reduce 48 mortality. In principle, this allows to compare the effectiveness of healthcare systems 49 across regions or countries. 50 The aim of this article is to analyze the effectiveness of the healthcare system in 51 France in the context of the COVID-19 epidemic. Based on spatial differences in 52 lethality, this study ultimately show that the case-fatality rates published by the 53 international agency by country (May 2020) do not allow to compared the country one 54 to the others. 55 Lethality depends on the intrinsic virulence of the virus but, unlike morbidity, it does 56 not depend on its contagiousness. Virulence comes from the reproductive capacity of 57 the virus in the cell, its capacity for cellular degradation, and its ability to induce or not 58 an innate or specific immune response. Virulence is of purely biological origin and once 59 the virus has entered the target cell where it will cause its pathogenic effect does no 60 longer depends on environmental conditions outside the host. Virulence is independent 61 of the host population, but may change over time and space if there is a risk of natural 62 mutation/selection of the pathogen. Contagiousness characterizes the biological 63 capacity of the virus to reach the target cell system of its host, and the ability to be 64 transmitted from one individual to another. The efficiency of transmission depends 65 largely on environmental conditions (e.g., climate, urbanization, population density, 66 mobility), which can vary greatly from one country to another. 67 In addition to the virulence of the virus, the case-fatality rate depends on biological 68 risk factors and on population vulnerability (age structure, genetic factors, prevalence 69 of co-morbidities, healthcare accessibility, etc.) as well as other factors related to the 70 health system (equipment, capacity, staff, management, care of patients, effectiveness 71 of therapies, patient management in a critical phase of the disease), and factors related 72 to the detection and registration system for cases and deaths (clinical cases definition, 73 detection, surveillance systems, case and death reporting). The evaluation of the case-74 fatality rate normally requires the detection and counting of all infected persons, 75 irrespective of their level of symptoms (i.e. disease severity). 76 When the population risk factors and reporting systems are identical, case-fatality 77 rate evaluation represents an excellent tool for analyzing, understanding and improving 78 the overall performance of the health system, particularly at the level of hospital units. 79 Studying the magnitude of differences in case-fatality rates between units also makes 80 it possible to assess the impact of the quality of the health system on case-fatality. 81 There are large differences in the case-fatality rates of COVID-19 published by 82 country (Table 1) or calculated directly from WHO data. These rates vary considerably, 83 from less than 0.02 (Thailand, Australia, Chile) to more than 0.15 (France, Belgium, 84 UK), with a mean at 0.04 and a standard deviation of 0.045 (WHO, May 8, 2020, Figure 85 1 CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 20, 2020. . In Europe, the characteristics of populations (in terms of risk factor for COVID-19) 96 and health systems are quite similar, but the definition, detection and reporting of cases 97 and causes of death can differ greatly from one country to another. Some countries 98 conducted significantly more detection tests and hospitalizations than others (Table 2) , 99 resulting in differences in the protocols for patient management. The rate of testing 100 performed (policy) and mortality rates (reporting) vary mainly according to the 101 geographical extent of the epidemic within each country. 102 103 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 20, 2020. The virulence of the COVID-19 pathogen (SARS-CoV-2 virus) is assumed to be 107 identical in all countries. In order to compare case-fatality rates across regions or 108 countries (and thus analyze the effectiveness of healthcare systems), it is necessary, 109 when calculating rates, to standardize population-related risk factors and to use the 110 same definitions and enumeration methods to record cases and deaths. This is not the 111 case for the current pandemic and discrepancies exist among the country systems. 112 The objective of this article is not to estimate the actual lethality of COVID-19 in 113 France based on the rates published by the health authorities, but to estimate the 114 influence of the healthcare system on lethality by analyzing the spatial variability of the 115 hospital case-fatality rate (confirmed hospitalized cases and hospital deaths) in 116 metropolitan France between districts (i.e. French départements). This analysis, 117 limited to metropolitan France, makes it possible while it remains within the framework 118 of the same system for defining and counting cases and deaths. We thus assume that 119 this system of definition and enumeration was identical throughout France during the 120 period (19 March to 8 May) corresponding to the first wave (inception) of the COVID-121 19 epidemic in France. Therefore the study focuses on the extent of spatial differences 122 in the case-fatality rate in metropolitan France, and enable to highlight the relative 123 differences between districts, as well as to analyze the causes independently of the 124 system of definition and enumeration of cases and deaths, and also independently of 125 the main biological risk factor of severity (age) after standardization on this factor. 126 Estimating the variability of the case-fatality rate attributable exclusively to hospital 127 care of patients will then allow us to compare the case-fatality rate observed in 128 metropolitan France with the one calculated for other countries. It will allow us to 129 estimate whether the variability of the case-fatality rate due to the management of 130 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2020. . https://doi.org/10.1101/2020.05.16.20104026 doi: medRxiv preprint patients in the acute epidemic phase can exclusively explain the significant differences 131 in case-fatality rates observed between countries. 132 This study is based on daily hospitalization and death declaration data by district 135 in France and is accessible on the "Santé Publique France" website. (www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-137 19) from March 19 to May 8, 2020, corresponding to 50 days lockdown (i.e. quarantine) 138 and the spread of the COVID-19 epidemic in France. We also obtain demographic data 139 by districts (source: population by age, INSEE, 2020), as well as data on the 140 distribution of hospitalized cases according to age group (10-year age group) (Santé 141 Publique France). This analysis was carried out on the 96 districts of metropolitan 142 France (Figure 2 ), while the French overseas districts and territories were excluded 143 from the analysis for reasons of spatial analysis and mapping. The data were 144 integrated into a geographic information system (SavGIS, ww.savgis.org) for analysis 145 and mapping. 146 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2020. severe and asymptomatic forms (which a fortiori do not cause deaths) -it is estimated 153 that only 2.6% of infected persons were hospitalized [SAL 20] -this overall lethality is 154 necessarily much lower than hospital lethality, but it will be accurately calculated only 155 at the end of the epidemic when the total number of positive cases (I.e. seroprevalence 156 survey) will be available and the total number of deaths outside hospital due to COVID-157 19 will be accurately assessed. 158 All identified and hospitalized cases were tested positive (by rtPCR). All deaths 159 counted were COVID-19 associated. As of May 8, 2020, not all hospitalized cases are 160 closed since the epidemic is still ongoing: deaths counted at the beginning of the study 161 period correspond to cases hospitalized but were not included in the study, and cases 162 counted at the end of the period were not closed and no deaths from these cases were 163 included in the study. 164 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2020. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2020. Belfort) (Figure 3) . 224 The hospital mortality rate (not age-standardized) has the same spatial distribution. 225 It varies from 0.01 per 1,000 (Tarn-et-Garonne) to 1.13 per 1,000 (Territoire de Belfort), 226 with a mean of 0.21 (median 0.12) and a standard deviation of 0.21. 227 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2020. from the calculation of age-specific case-fatality rates, the SLR is between 0.28 and 255 1.67, with the mean at 0.99 and the median at 1 (Figure 7) . In the following, we will 256 consider only the SLRs calculated with age-specific case-fatality rates that do not take 257 into account the Ile-De-France and Grand-Est regions. 258 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2020. The spatial distribution of standardized morbidity rate (hospitalized cases) shows 261 a significant spatial autocorrelation (Moran index: 1.54, p-value < 10 -6 ), and this is 262 expected for an infectious disease. The case-fatality rate shows also significant spatial 263 autocorrelation (Moran index: 0.29, p-value < 0.000007), and this is no expected. The 264 analysis of the clusters clearly shows a clustering of high case-fatality rate values in 265 regions of high morbidity (particularly the Grand-Est), and shows some cases of 266 districts with high case-fatality rate values isolated in areas with low rates. 267 The Breslow & Day significance test shows districts where the SLR is statistically 268 significantly different from 1, corresponding to districts with abnormally high (SLR > 1, 269 red) or abnormally low (SLR < 1, green) case-fatality rates. The individual significance 270 threshold is set at 0.05, and for all districts at 0.0005 to account for multi-testing ( Figure 271 8). 272 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. There is a correlation between the standardized hospitalization rate and the 277 standardized case-fatality rate (Bravais-Pearson index=0.40) (Figure 9 ), a correlation 278 which increases (0.48) if we limit the calculation to districts whose SLR is significantly 279 different from 1 (p-value < 0.05). To illustrate the increase of case-fatality rate with hospitalization rate, Table 4 gives 284 the mean of the standardized case-fatality rate over the districts according to their 285 standardized hospitalization rate. The average case-fatality rate varies from 0.134 for 286 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2020. The mapping of the hospitalization rate and the hospital mortality rate with the SLR 296 shows the spatial correspondence of these values ( Figure 10) . A typology combining hospitalization rates and case-fatality rates is proposed: low 302 rates (values below the mean by less than one standard deviation), high rates (values 303 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2020. . above the mean by more than one standard deviation), so as to represent four classes 304 (low-low, low-high, high-low, high-high). The hatched areas represent those for which 305 the SLR is not significant (p-value > 0.05) (Figure 11 ). 306 307 Figure 11 . Combination of standardized hospitalization and case-fatality rates in four classes. The ratio between the rate of patients in intensive care and the rate of 309 hospitalization gives in principle an indication of the severity of the patients in hospital. This hospitalization rate and severity rate show a weak negative correlation (r=-0.22), 311 indicating a decrease in the intensive care rate when the hospitalization rate is high. 312 This trend may be due to the saturation of intensive care units. The relationship 313 between hospitalization and severity could also be interpreted as a decrease in less 314 severe hospitalizations in order to be able to manage more severe cases when the 315 healthcare system is overloaded, which would result in an increase in lethality. Nevertheless, in both cases, there is no correlation between the severity rate and the 317 case-fatality rate (r=-0.1), indicating that globally, the intensity of reanimation does not 318 impact the case-fatality rate. Finally, the severity rate does not have a spatial 319 distribution corresponding to the increase in the hospitalization rate ( Figure 12) . 320 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The standardized case-fatality rates (SLR) of the districts in France (0.04 for Tarn-325 et-Garonne to 0.26 for the Vosges district) remain in a ratio of 0.3 to 1.6 compared with 326 the national average of 0.14, calculated by excluding districts under stress in order not 327 to take account of possible saturation of the care systems. The relationships between 328 morbidity rates and standardized case-fatality rates in France show a correlation 329 between these two indices, the average case-fatality rate for all districts being about 330 15% higher than the average rate calculated in the 20% of districts with the lowest 331 hospitalization rates. It is therefore very likely that the increase in hospital tension over 332 the period under consideration has increased the hospital case-fatality rate: for the 20 333 districts with the highest hospitalization rates (essentially located in the Grand-Est and 334 Ile-De-France regions), the average case-fatality rate is 20 per cent higher than the 335 average for all districts, and 25 per cent higher than the average for all other districts 336 alone. It can be concluded that hospital case-fatality rates have increased the national 337 average case-fatality rate by district from 0.145 to 0.153. It can therefore be estimated 338 that 2,425 deaths (out of the 16,732 deaths due to COVID-19 in hospital in France from 339 19 March to 8 May 2020, i.e. 15% of the total number of deaths) are due to the 340 saturation of the health system in the Grand-Est and Ile-De-France regions. 341 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2020. . https://doi.org/10.1101/2020.05.16.20104026 doi: medRxiv preprint There are clearly two risk profiles: 1/ the districts where a high rate of 342 hospitalization is coupled with a high case-fatality rate, and 2/ the districts where a low 343 rate of hospitalization is coupled with a high case-fatality rate. The first category 344 probably results from an increase of lethality due to saturation of the health care 345 system. The second category is probably linked to the opposite phenomenon: a low 346 hospital case-fatality rate which would have led to an increase in lethality due to a local 347 lack of healthcare access (e.g. medical deserts, poor hospital lethality preparation). It 348 has also been noted that all these late districts are located in essentially rural areas. 349 Some districts in the south of France have both a very low rate of hospitalization and 350 a very low case-fatality rate (Gironde, Dordogne, Gers, Pyrénées Orientales), as a 351 result of the low circulation of the virus and the effective response of the health system. 352 Another particular case, is the one of the Bouches-du-Rhône, which appears with a 353 high hospitalization rate ( French average is therefore very significantly higher than the world average (p-value 380 < 10-6). Even if we consider only the average case-fatality rate calculated only for the 381 French districts with the lowest hospitalization rates (thus not causing saturation of the 382 health care system), this average is still very significantly higher than the international 383 average (and the rates of most European countries, such as Spain, 11.73, Greece, 384 5.52, Germany, 4.28, etc.) (table 4) . Taking into account the quality of the healthcare 385 system in France (table 1) , it can be concluded that the difference between the case-386 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 20, 2020. . fatality rate calculated for France and the case-fatality rates presented using 387 international WHO data is highly probably the result of a difference in the registration 388 of cases and/or deaths and not due to the quality of health care. These differences in 389 the counting of cases and/or deaths may be due to the hospitalization and screening 390 policy specific to each country as well as the ability or willingness to hospitalize more 391 non-severe forms, to the differences in case definition, or to insufficient quality of the 392 system for detecting and reporting cases and deaths. 393 This study shows that the higher case-fatality rates observed in France in certain 395 districts during the first wave of the COVID-19 epidemic (data from 19 March to 8 May 396 2020) are mostly linked to the level of morbidity in the district, and therefore to the 397 congestion of the healthcare systems during the acute phases of the epidemic. When 398 the hospitalization rate is low, high case-fatality rates concern rural districts and could 399 be linked to health care access in these districts. 400 However, the increase in the standardized case-fatality rate due to exceptional 401 situations during epidemic peaks represents less than 10% of the average case-fatality 402 rate per district in France, and the hospital case-fatality rate without these districts 403 would be reduced from 0.153 to 0.145. This increase cannot therefore explain the 404 extent of the difference observed between the average case-fatality rate in France and 405 the average of the rates reported for all countries by international organizations or 406 information sites (WHO, Wordometer, etc.). These differences probably stem from the 407 reporting of cases and deaths, which is uneven from one country to another, and not 408 from the care or treatment of patients during hospital stress due to epidemic peaks. 409 Real estimates of 411 mortality following COVID-19 infection 414 [MOR 20] Morteza Abdullatif Khafaie, Fakher Rahim. Cross-Country Comparison of Case 415 Fatality Rates of COVID-19/SARS-COV-2. Osong Public Health and Research 416 POR 08] Porta M. A dictionary of Epidemiology. 5th Ed An empirical estimate of the infection fatality 420 rate of COVID-19 from the first Italian outbreak Using Early Data to Estimate the Actual 422 Infection Fatality Ratio from COVID-19 in France Estimating the burden of SARS-CoV-2 in France