Summary
Background
The objective of this study was to better understand the factors associated with the heterogeneity of in-hospital COVID-19 morbidity and mortality across France, one of the countries most affected by COVID-19 in the early months of the pandemic.
Methods
This geo-epidemiological analysis was based on data publicly available on government and administration websites for the 96 administrative departments of metropolitan France between March 19 and May 11, 2020, including Public Health France, the Regional Health Agencies, the French national statistics institute, and the Ministry of Health. Using hierarchical ascendant classification on principal component analysis of multidimensional variables, and multivariate analyses with generalised additive models, we assessed the associations between several factors (spatiotemporal spread of the epidemic between Feb 7 and March 17, 2020, the national lockdown, demographic population structure, baseline intensive care capacities, baseline population health and health-care services, new chloroquine and hydroxychloroquine dispensations, economic indicators, degree of urbanisation, and climate profile) and in-hospital COVID-19 incidence, mortality, and case fatality rates. Incidence rate was defined as the cumulative number of in-hospital COVID-19 cases per 100 000 inhabitants, mortality rate as the cumulative number of in-hospital COVID-19 deaths per 100 000, and case fatality rate as the cumulative number of in-hospital COVID-19 deaths per cumulative number of in-hospital COVID-19 cases.
Findings
From March 19 to May 11, 2020, hospitals in metropolitan France notified a total of 100 988 COVID-19 cases, including 16 597 people who were admitted to intensive care and 17 062 deaths. There was an overall cumulative in-hospital incidence rate of 155·6 cases per 100 000 inhabitants (range 19·4–489·5), in-hospital mortality rate of 26·3 deaths per 100 000 (1·1–119·2), and in-hospital case fatality rate of 16·9% (4·8–26·2). We found clear spatial heterogeneity of in-hospital COVID-19 incidence and mortality rates, following the spread of the epidemic. After multivariate adjustment, the delay between the first COVID-19-associated death and the onset of the national lockdown was positively associated with in-hospital incidence (adjusted standardised incidence ratio 1·02, 95% CI 1·01–1·04), mortality (adjusted standardised mortality ratio 1·04, 1·02–1·06), and case fatality rates (adjusted standardised fatality ratio 1·01, 1·01–1·02). Mortality and case fatality rates were higher in departments with older populations (adjusted standardised ratio for populations with a high proportion older than aged >85 years 2·17 [95% CI 1·20–3·90] for mortality and 1·43 [1·08–1·88] for case fatality rate). Mortality rate was also associated with incidence rate (1·0004, 1·0002–1·001), but mortality and case fatality rates did not appear to be associated with baseline intensive care capacities. We found no association between climate and in-hospital COVID-19 incidence, or between economic indicators and in-hospital COVID-19 incidence or mortality rates.
Interpretation
This ecological study highlights the impact of the epidemic spread, national lockdown, and reactive adaptation of intensive care capacities on the spatial distribution of COVID-19 morbidity and mortality. It provides information for future geo-epidemiological analyses and has implications for preparedness and response policies to current and future epidemic waves in France and elsewhere.
Funding
None.
Introduction
Spatial differences between incidence and mortality rates have been associated with factors as various as the arrival time of SARS-CoV-2,
population age structure,
urban development and population density,
economic level,
health system,
climatic and meteorological factors,
and anti-contagion policies and practices.
,
,
but the first three patients prospectively diagnosed with COVID-19 were reported on Jan 24, 2020, in Bordeaux and Paris, all returning from Wuhan (China).
,
,
In February, 2020, four clusters were reported in other areas, including Haute-Savoie, Oise, Morbihan, and Haut-Rhin. These clusters were mainly due to contact with people travelling from Singapore, China, Italy, or Egypt, and no specific spatial trends were identified at this stage. In Mulhouse city (Haut-Rhin), one religious event that took place from Feb 17 to Feb 21, 2020, brought together 2000–2500 participants from all over France and several other countries (eg, Belgium, Switzerland, Germany, and Burkina Faso). Many attendees became infected and then spread the virus onwards when returning home. The Oise cluster, near Paris, appeared in late February around a military airbase employing around 2500 people. Several airbase staff had been involved in the repatriation of a French citizen from China on Jan 31, 2020.
and eventually extended until May 11. The daily incidence peak was reached on March 31, with 7578 new confirmed cases. However, considering the low level of testing capacities in France at this time of the epidemic, a model suggests that as many as 300 000 daily new infections might have occurred right before the lockdown onset.
Hampered by the lockdown, the epidemic reached the rest of the country more slowly. In these other departments, the first deaths generally occurred after lockdown implementation, except for specific departments such as Corse-du-Sud (Corsica Island) and Morbihan (Brittany region). In the southeastern region, the most densely populated department (Bouche-du-Rhône including Marseille, the second largest French city) was more affected than the surrounding ones, in terms of incidence and mortality rates.
Evidence before this study
The spread of the COVID-19 pandemic has shown important heterogeneity between countries and regions, and France was one of the most affected countries in the early months of the pandemic. To assess the factors associated with the spatial differences between incidence and mortality rates, we searched PubMed for all articles up to Sept 28, 2020, using the query (COVID-19 OR SARS-CoV-2) AND ((France Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study AND epidemiology) OR (France Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study AND lockdown) OR (lockdown Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study AND (effect Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study OR effectiveness Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study OR impact Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study)) OR “ecological study” OR (socioeconomic Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study OR demography Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study OR demographic Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study OR climate Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study OR climatic Factors associated with the spatial heterogeneity of the first wave of COVID-19 in France: a nationwide geo-epidemiological study)). Some articles describing the initial COVID-19 clusters in France presented results of mechanistic or agent-based models at a coarse spatial scale, but none of the 456 articles described the first pandemic wave in France and analysed associated factors at a fine spatial scale. Similarly, the effect of the national lockdown in France was studied by only a few mechanistic or agent-based models. Worldwide, the importance of lockdown policies to drastically decrease COVID-19 cases and deaths was mostly suggested by model predictions, or by a few inter-country epidemiological comparisons. The influence of population age structure on COVID-19-associated mortality was highlighted at the country level but not studied at finer scales. COVID-19 incidence and mortality appeared to be higher in countries with a high socioeconomic status, but within affected countries or territories, populations with low socioeconomic status generally reported lower cases and mortality, suggesting a frequent testing and reporting bias.
Added value of this study
This geo-epidemiological multivariate analysis confirmed that the marked spatial heterogeneity of in-hospital COVID-19 cases and deaths across metropolitan France was strongly associated with the initial spread of the first pandemic wave before its efficient freezing by the national lockdown. Case fatality rate was not associated with the initial number of intensive care beds, suggesting that hospitals could rapidly scale up their capacities and organise medical evacuations to less affected areas. We found no independent association between in-hospital COVID-19 incidence and the four climates of metropolitan France.
Implications of all the available evidence
Epidemiological analyses should include COVID-19 pandemic spread, demographic structure, mitigation measures, and health-care capacities. All countries should adapt their preparedness plans to these key factors to better respond to current or future pandemic waves.
no study has yet analysed the underlying combination of determinants of this spatial heterogeneity. We therefore did a geo-epidemiological analysis using data that were publicly available on government and administration websites to study the ecological factors associated with in-hospital COVID-19 incidence, mortality, and case fatality rates across France during the national lockdown. Our objective was to better understand the factors potentially associated with the heterogeneity of in-hospital COVID-19 morbidity and mortality across the country.
Results
Figure 1Spatial heterogeneity of COVID-19 in France, showing cumulative in-hospital incidence (A), in-hospital mortality rate (B), and in-hospital case fatality rate (C)
Figure 2Maps of covariates, showing population age structure (A), climate classes (B), urbanisation (C), economic profile (D), population health and health-care services (E), the lag between the first COVID-19-associated death and lockdown (F), baseline intensive care capacity (G), and chloroquine and hydroxychloroquine dispensations in pharmacies (H)
Table 1Factors associated with in-hospital COVID-19 incidence rate at the department level in metropolitan France
Analyses were made using generalised additive models with a negative binomial regression, a Gaussian kriging smoother based on geographical coordinates, and log(population) as an offset. The multivariate model included confounders according to the directed acyclic graph. aSIRs were adjusted on the different cofactors and spatial structure. SIR=standardised incidence ratio. aSIR=adjusted standardised incidence ratio. Ref=reference.
Table 2Factors associated with in-hospital COVID-19 mortality rate at the department level in metropolitan France
Analyses were made using generalised additive models with a negative binomial regression, a Gaussian kriging smoother based on geographical coordinates, and log(population) as an offset. The multivariate model included confounders according to the directed acyclic graph. aSMRs were adjusted on the different cofactors, spatial structure, and the interaction between COVID-19 cases and temporal progression of the epidemic wave (p=0·062). SMR=standardised mortality ratio. aSMR=adjusted standardised mortality ratio. Ref=reference.
Table 3Factors associated with in-hospital COVID-19 case fatality rate at the department level in metropolitan France
Analyses were made using generalised additive models with a negative binomial regression, a Gaussian kriging smoother based on geographical coordinates, and log(population) as an offset. The multivariate model included confounders according to the directed acyclic graph. aSFRs were adjusted for the different cofactors and spatial structure. SFR=standardised fatality ratio. aSFR=adjusted standardised fatality ratio. Ref=reference.
Discussion
and Di Domenico and colleagues
estimated that the lockdown reduced the reproductive number by 77% in France and 81% in Île-de-France. Using databases across 149 countries, Islam and colleagues
also estimated that earlier implementation of lockdown was associated with a larger reduction in the incidence of COVID-19.
in-hospital mortality and case fatality rates were higher in departments with older populations. Age has indeed been identified as the main risk factor of COVID-19 disease severity and death in many cohort studies.
,
We did not include the prevalence of comorbidities such as diabetes or obesity in our study because of a scarcity of available data at the departmental level. However, we considered several indicators associated with overall population health, such as basal mortality rate and the usual proportion of hospital stays in endocrinology, cardiology, pneumology, and medicine wards.
,
However, our findings do not support this hypothesis because chloroquine and hydroxychloroquine dispensations were not associated with in-hospital incidence, mortality, or case fatality rates at the departmental level. We could not include data for in-hospital dispensations, which are not publicly available in France, and the prescription of chloroquine and hydroxychloroquine for COVID-19 was limited by a French regulation on May 26, 2020, after the end of our study period. Although univariate analyses showed important associations between in-hospital incidence and mortality rates and new chloroquine and hydroxychloroquine dispensations, these associations were positive (ie, higher chloroquine and hydroxychloroquine dispensations were associated with higher incidence or mortality rates). This association suggests a common confounding by indication,
which was corrected by the multivariate analysis. Furthermore, our multivariate analysis explained almost all in-hospital COVID-19 incidence in the Paris and Bouches-du-Rhône departments, the two departments where chloroquine and hydroxychloroquine dispensations were the highest. The observed mortality in Bouches-du-Rhône was even higher than predicted by our model. Nevertheless, our ecological study was not designed to assess the effectiveness of chloroquine and hydroxychloroquine against COVID-19 at the individual level; these drugs have been shown not to be effective.
The age of patients admitted to hospital for COVID-19 has not been made available at the departmental level to account for these differences. This factor could partly account for why our multivariate analysis explained only 44·6% of the case fatality rate. Additional factors might also have been associated with case fatality rate, suggesting a need for further analysis at a more accurate scale. For instance, it was not possible to analyse ethnicity, because this characteristic is not recorded in French databases for legal reasons.
nor interpret the results in terms of causality. Moreover, the multidimensional reduction of economic indicators using hierarchical ascendant classification on principal component analysis, which allows numerous factors to be assessed and considers collinearities and the curse of dimensionality, might also have flattened the differences between departments and hidden possible associations.
In conclusion, our findings outline the effect of the COVID-19 pandemic wave in a country that could absorb the shock, thanks to a strong hospital system and a national lockdown. However, the findings indirectly underscore the weakness of its preventive and public health system, which could be useful for informing countries’ preparedness for the current or future pandemic waves.
JG and SR developed the study idea and the analysis plan. JG, JL, LH, EM, FK-S, JD, LC, RP, and SR contributed to the literature review. JG, JL, MKB, and SR did the data investigation and management. All data were publicly available and all authors had full access to the data and analyses, and were responsible for the final decision to submit for publication. JG, JL, and SR were responsible for verifying the data used in the study. EL, LL, and AP participated in the data management and analysis. JG did the statistical analysis. JG and SR wrote the first draft. All authors contributed to the interpretation of the findings, reviewed the analysis, wrote the manuscript, and approved the final manuscript. JG had the final responsibility for the decision to submit for publication.
We declare no competing interests.
