key: cord-0869124-eln7n6zb authors: Ciminelli, G.; Garcia-Mandico, S. title: How Emergency Care Congestion Increases Covid-19 Mortality: Evidence from Lombardy, Italy date: 2020-10-29 journal: nan DOI: 10.1101/2020.10.27.20221085 sha: d7511309213db77a0b5eacb42d2f7058821351b6 doc_id: 869124 cord_uid: eln7n6zb BACKGROUND: The Covid-19 pandemic has caused generous and well-developed healthcare systems to collapse. This paper quantifies how much system congestion may have increased mortality rates, using distance to the ICU as a proxy for access to emergency care. METHODS: We match daily death registry data for almost 1,500 municipalities in Lombardy, Italy, to data on geographical location of all ICU beds in the region. We then analyze how system congestion increases mortality in municipalities that are far from the ICU through a differences-in-differences regression model. FINDINGS: We find that Covid-19 mortality is up to 60% higher in the average municipality -- which is 15 minutes driving away from the closest ICU -- than in a municipality with an ICU in town. This difference is larger in areas and in days characterized by an abnormal number of calls to the emergency line. INTERPRETATION: We interpret these results as suggesting that a sudden surge of critical patients may have congested the healthcare system, forcing emergency medical services to prioritize patients in the most proximate communities in order to maximize the number of lives saved. Through some back-of-the-envelope calculations, we estimate that Lombardy's death toll from the first Covid-19 outbreak could have been 25% lower had all municipalities had ready access to the ICU. Drawing a lesson from Lombardy's tale, governments should strengthen the emergency care response and palliate geographical inequalities to ensure that everyone in need can receive critical care on time during new outbreaks. The first wave of Covid-19 strained healthcare systems in many countries, abounding a series of lessons for governments to prepare for new outbreaks. The principal lesson has highlighted the need to strengthening hospitals and intensive care units (ICUs) capacity. As such, policies have so far mostly focused on expanding already existing emergency infrastructure and building field hospitals near existing medical centres. 1 While necessary, this does little to reduce geographical inequalities in emergency care coverage. This paper highlights the need of tackling such disparities by quantifying the mortality effects of having communities underserved by emergency care when the system is under severe strain. The analysis focuses on Lombardy, which offers a good case to study. The region is one of Italy's wealthiest and is also renowned for having one of the best healthcare infrastructures in the country, which itself has fairly high rates of ICUs per capita. 2 Yet, Lombardy suffered extremely high mortality rates during its first Covid-19 outbreak, which cannot be explained by traditional epidemiological models, such as SIR and SEIR. 3, 4, 5 These models feature an exogenous probability of dying once individuals become infected. However, as noted by Favero, if the healthcare system is saturated and infected people cannot access to the ICU, such probability increases, becoming endogenous to the level of system congestion. 6 This paper analyzes how much system congestion may have contributed to the high mortality rates observed during Lombardy's first Covid-19 outbreak, using distance to the ICU as a proxy for access to emergency care. To carry out the analysis, we match highly granular daily death registry data for almost 1,500 municipalities to information on geographical location and number of all ICU beds across Lombardy. We complement the dataset with daily data on the volume of calls to the emergency line, as well as data on a number of co-factors of Covid-19 mortality. We start by showing that, despite its generosity, the region's healthcare system is characterised by significant disparities: the average distance in minutes of driving to the nearest ICU is three times as large for municipalities in the mountainous subregion of the Alps 1 as for those in the metropolitan area of Milan. This is important because, when the system is overwhelmed and there is not enough time to attend everyone in need, emergency medical services may have to prioritize patients in the most proximate communities, at the expense of reducing geographical coverage, in order to maximize the number of lives saved. 7 To test whether distance to the ICU has any effect on Covid-19 mortality, we develop a differences-in-differences regression model. We find that mortality is up to 60% higher in the average municipality -which is 15 minutes of driving away from the closest ICU -than in a municipality with an ICU in town. Of course, distance on its own does not imply that critical patients cannot get to the ICU on time. After all, some patients require transportation from remote communities to the ICU also in normal times, and usually they get there on time. Distance to the ICU only becomes a determining factor when the burden on the emergency care system is high, as capacity to serve everyone in need is reduced. We proxy for system congestion using data on calls to the emergency line and find that the additional effects of Covid-19 on mortality in municipalities that are farther away from the ICU is stronger in days and areas characterized by an abnormal volume of calls to the emergency line, pointing to system congestion as a plausible explanation. We then quantify how much system congestion may explain the high mortality rates observed in Lombardy's first Covid-19 outbreak. To do so, we perform some back-of-theenvelope calculations to compare the number of deaths that occurred in actuality to those that would have occurred in a hypothetical scenario in which all communities had an ICU in town. We find that Covid-19 deaths would have been about 25% less in such as a scenario, meaning that many lives could have been saved through more widespread critical care coverage. The rest of the paper is organized as follows. We close Section 1 by putting our analysis in context. Section 2 describes the dataset and methodology. Section 3 presents the results and Section 4 concludes. 2 . 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 October 29, 2020. ; https://doi.org/10.1101/2020. 10.27.20221085 doi: medRxiv preprint Research in context Evidence before this study Epidemiological models such as SIR and SEIR are at the center of a quickly growing literature assessing how mitigation policies can be optimally set to minimize the burden on the economy while reducing the number of fatalities. However, these models fail to predict the mortality observed in outbreak epicenters, where healthcare systems, and in particular emergency care systems, are overwhelmed. Another strand of the literature seeks to understand the causes behind the severity of Italy's first Covid-19 outbreak. We contribute to these two strands of work by focusing on the congestion of the healthcare system as an important reason behind the high death toll of Lombardy's first Covid-19 outbreak, which cannot be explained by existing epidemiological models. To our knowledge, this is the first study to quantify how an uneven distribution of ICUs across communities may increase Covid-19 mortality when the health system is overburdened. Our resultswhich are robust to controlling for a host of co-factors of Covid-19 mortality -indicate that geographical differences in healthcare coverage, together with overwhelmed healthcare systems, account for a significant fraction of the observed Covid-19 mortality in the region. 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 October 29, 2020. ; https://doi.org/10.1101/2020. 10.27.20221085 doi: medRxiv preprint should also invest in building ambulance capacity and, ideally, mobilize ICUs more evenly across the territory. All these factors are key to help reducing mortality in the new waves of Covid-19. We source death registry and census population data at the municipality-level from ISTAT, the Italian Statistical Agency. 8, 9 Census data provide information on the resident population as of January/1 st , while death registry data provide information on daily deaths for the January/1 st to May/15 th period for the years 2015 to 2020, for almost all Italian municipalities. To measure Covid-19 mortality, we rely on the concept of excess deaths -that is, the difference between deaths for all causes during the Covid-19 epidemic and deaths that would be expected under normal circumstances. We prefer this approach over using official Covid-19 fatality data because these vastly undercount the real number of Covid-19 deaths, as we show in a companion paper. 10 Moreover, focusing on excess deaths has the key advantage that underlining data are much more granular than official fatality data, which is crucial for our identification strategy, as it will become clear below. Our focus is on the Lombardy region, which is universally considered as Europe's ground zero for Covid-19 and the epicenter of Italy's first outbreak, making up for about 50% of all fatalities, with less than 17% of the overall population. The data cover 1,455 municipalities, together accounting for almost 98% of Lombardy's population. 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 October 29, 2020. ; of daily calls to the emergency line, by subregion and reason for the call. 11 These data are taken from L'Eco di Bergamo. 12 We also source data on location and areas of specialization of each hospital and private clinic from Lombardy's institutional database. 13 We use this information to construct a municipality-level variable measuring distance to the nearest ICU. If there is an ICU in town, we set it to zero. Otherwise, the variable measures the distance, in minutes of driving, to the nearest municipality with an ICU (taken from ISTAT). 14 Admittedly, ICU capacity has been strengthened during the Covid-19 epidemic and these additional emergency ICU beds are not recorded in our data on health facilities, which provide a snapshot as of end-2019. However, this does not impact our distance-to-ICU measure because capacity expansion was concentrated in municipalities that already had ICUs (mostly in Bergamo, Crema, and Milan). The dataset is complemented with variables capturing slow-moving socio-demographic, labor market and territorial characteristics that we use to control for potential co-factors of Covid-19 mortality. As most of these variables are not available at a regular frequency, we compute their means over the 2015-2019 period and treat them as time-invariant factors. Appendix Table A2 provide detailed information on their sources and coverage. Next, we discuss a few stylized facts emerging from the data. Before the detection of the first community case, deaths in 2020 match very closely deaths in 2016 (see Appendix Figure A1 ). The severe effects of Covid-19 on mortality is underscored by the exponential increase in deaths following the detection of the first community caseat their peak, roughly a month after detection, deaths in 2020 are about four times as large as deaths in 2016. Also worth noting, excess mortality reaches particularly high levels in the Alps and the Po Valley subregions (see Appendix Figure A2 ). Overall, the virus may have contributed to the death of up to 0.4% of the local population in the Alps, and about 0.3% 5 . 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 October 29, 2020. ; https://doi.org/10.1101/2020.10.27.20221085 doi: medRxiv preprint in the Po Valley. We then move on to the emergency care system. Figure 1 below characterizes municipalities according to distance to the closest ICU. Only less than 4% of all municipalities have an ICU in town. As expected, these tend to be the larger municipalities. However, together they only account for less than a quarter of Lombardy's population. The average municipality is 15 minutes away from the closest one with an ICU in town. But this figure masks large disparities between the subregions. Mean distance to the ICU is reduced to just 7.5 minutes for municipalities in the Metropolitan subregion, while about a quarter of all municipalities in the Alps subregion are further than 25 minutes away, meaning 6 . 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 October 29, 2020. that an ambulance roundtrip may take more than an hour. Calls to the emergency line more than doubled in the Alps subregion Overall in Lombardy, calls for respiratory reasons surge more than four-fold in the month following the detection of the first community case (denoted by the vertical line), from about 3 to almost 14 per 100,000. After peaking, they decrease very slowly, returning to pre-Covid-19 levels only three months following detection (Panel A1). The increase in total calls (calls 7 . 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 October 29, 2020. ; https://doi.org/10.1101/2020.10.27.20221085 doi: medRxiv preprint for any reason) is more nuanced and the reversal to pre-Covid-19 levels happens much earlier, likely due to seasonal factors and the fact that the lockdown imposed by the government to contain the epidemic reduced road and workplace accidents (Panel A2). Panels B1 and B2 zoom in on calls received in each of the four subregions. While in the Alps calls for respiratory reasons surge more than 8-fold and total calls double, the increase in calls for respiratory reasons is less than 5-fold in the Po Valley and less than 4-fold in the Metropolitan and the Lakes subregions. In the latter two the total volume of calls barely increase. All in all, this visual inspection of the data suggests that, although calls for respiratory reasons increased across the board, the Alps subregion -which is also the one with the most uneven distribution of ICUs -may have particularly struggled to cope with the surge in demand for critical care. In what follows we illustrate the methodology used to formally analyze the insights that emerged from this first look of the data. We first quantify the effect of Covid-19 on the mortality rate. To do so, we follow closely the methodology that we developed in two companion papers. 10, 15 Specifically, we rely on a differences-in-differences approach to estimate the dynamic effects on the mortality rate, using the year 2016 as counterfactual of what mortality would have been in absence of Covid-19. The choice of using the year 2016 follows from a visual inspection of the data (see Appendix Figure A1 ), but the results are robust to using mean 2015-2019 mortality as alternative counterfactual. The equation that we estimate is as follows: where y ijt measures daily deaths per 100,000 inhabitants in municipality i, at within-year time t, for year j ; d 2020 j is a dummy variable taking value equal to 1 in 2020 and 0 otherwise; 8 . 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 October 29, 2020. ; https://doi.org/10.1101/2020.10.27.20221085 doi: medRxiv preprint DAY t are within-year time effects, taking value 1 in each particular day of the year and 0 otherwise; µ i are municipality fixed effects; and ε ijt is an idiosyncratic error, clustered at the municipality-level. The summation term ranges from -20 to +85 since we normalize the within-year time dimension t so that it takes value equal to 0 on the day in which the first community case was detected (February/21 st ) and negative (positive) values on days before (after). For the estimation, we use the least squares method with population analytical weights. Appendix Figure A3 depicts the effect of Covid-19 on mortality, obtained plotting theβ t coefficients estimated from Equation (1). The effect peaks exactly a month after onset, at slightly below 8 deaths per day per 100,000 inhabitants and then slowly decreases, until becoming statistically insignificant 44 days after the peak. These estimates are robust to using average deaths in the five preceding years (2015 to 2019) as counterfactual (also reported in Appendix Figure A3 ). Next, we turn to the congestion of the emergency care system as one potential factor that may have increased mortality. We start by uncovering a positive relationship between daily mortality rates and the variable measuring distance to ICU (see Appendix Figure A4 ). More precisely, being 10 minutes farther away from the ICU is associated with about 1 more death per 100,000 inhabitants per day, on average, during the entire epidemic period. This relationship is highly statistically significant. To more formally estimate the effects of the uneven distribution of ICUs on mortality, we extend Equation (1) by adding an interaction term between the within-year effects, the year 2020 dummy and our variable measuring distance to the ICU. More specifically, the equation that we estimate is as follows: 9 . 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 October 29, 2020. ; https://doi.org/10.1101/2020.10.27.20221085 doi: medRxiv preprint where dist ICU i is the variable measuring distance, in municipality i, to the closest municipality with an ICU in town; and the rest of the notation is as before. As for Equation (1) above, the estimation is through OLS with standard errors clustered at the municipality-level. Up to 60% higher mortality in municipalities 15 minutes away from the ICU The average municipality experiences significantly higher mortality rates than those with an ICU in town at the height of the epidemic. At peak, the effect of Covid-19 on the mortality rate reaches 6 deaths per day per 100,000 inhabitants in municipalities with an ICU in town, while the same effect is about 10 deaths per day per 100,000 inhabitants, or over 60% higher, in the average municipality, which is 15 minutes away from the ICU. The divergence in the mortality effect of Covid-19 across these two groups of municipalities match very closely the evolution of calls to the emergency line. Particularly, the additional effect in municipalities that do not have an ICU in town starts decreasing shortly after that the volume of calls to the emergency care eases up, and it becomes statistically insignificant once emergency calls return to pre-Covid-19 levels. How can we explain the result that Covid-19 mortality rates are higher in communities that are more distant from the intensive care? One possibility is that the emergency care system struggled to cope with a surge in demand. Indeed, Sorbi reports that waiting times for emergency transportation swelled: to make a trip that usually took only 8 minutes, ambulances were taking an hour, and in some cases, they were not getting in on time. 16 . 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 October 29, 2020. ; This begs the question of how many deaths could have been prevented if all communities were readily served by an ICU. To answer this question, we use the coefficients estimated in Equation (2) and perform some back-of-the-envelope calculations. First, we calculate the overall number of deaths that can be ascribed to Covid-19 in more than 26,000. Then we calculate the number of Covid-19 deaths in a hypothetical counterfactual scenario in which every municipality had an ICU in town. We find that deaths would have been slightly less 11 . 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 October 29, 2020. ; than 20,500 in this counterfactual scenario. This means that about 28% of all Lombardy's deaths in the first Covid-19 outbreak could have been prevented through better ICU coverage. Before proceeding further, we check that our results are robust to controlling for other municipality characteristics that may correlate with distance to the ICU and that could also have an effect on mortality, such as population density, income, education, the demographic structure, the share of people employed in the healthcare sector, the number of ICU beds per capita, the share of people employed in essential sectors, distance from the epicenter and others. We also verify that our results are not driven by outliers and exclude municipalities with abnormal mortality rates and those in which the closest ICU is very far away. Finally, we check that our estimates are robust to using an alternative variable measuring distance to the ICU, in kilometers rather than minutes (taken from ISTAT). 14 All the results from these robustness specifications are shown in Appendix Figure A5 . For simplicity, the figure shows the additional effects of being 15 minutes away from the ICU, given by theπ t coefficients estimated from Equation (2), and compares it with the same effects estimated from the alternative specifications discussed above. Overall, the new estimates are very close to, and not statistically different from, our baseline, thus confirming the validity of our results. Larger effects when and where the emergency care system is congested Next, we zoom in on the Alps subregion, where calls to the emergency line for respiratory reasons increased more than 8-fold and total calls more than doubled (see Figure 2 above), putting particularly high pressure on the emergency care system. If indeed distance to the ICU increases mortality when the system is congested, we should find larger effects in the Alps than in the other subregions, in which the increase in emergency calls was less pronounced. To formally test this hypothesis, we twist Equation (2) to estimate two distinct sets of coefficients measuring the additional effect of distance to the ICU on mortality, one for the Alps subregion and the other for the rest of Lombardy. Figure 4 below shows these newly 12 . 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 October 29, 2020. ; estimated coefficients. For simplicity, the figure only reports the additional effect of Covid-19 on mortality in municipalities that are 15 minutes away from the ICU relative to those with an ICU in town. (2), which allows for a differential effect of distance to ICU on mortality in the Alps subregion vs. the rest of Lombardy. Blue solid lines denote the estimated effect, shaded areas are 95% confidence intervals, while dashed black lines depict the daily volume of calls to emergency system. The y-axes report daily deaths and calls to the emergency line per 100,000 inhabitants (left and right axes respectively). The x-axis reports days after onset. The extra effect of being distant from the ICU on Covid-19 mortality is concentrated in the Alps subregion. There, municipalities that are 15 minutes away from the ICU experience up to 8 more deaths per day per 100,000 inhabitants. For the rest of Lombardy, instead, we do not estimate any statistically significant difference in mortality between municipalities that are far from the ICU and those with an ICU in town. . 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 October 29, 2020. ; https://doi.org/10.1101/2020. 10.27.20221085 doi: medRxiv preprint Looking at the volume of calls to the emergency line, we note that the dynamics of the extra effect of being distant from the ICU closely follow the evolution of emergency calls in the Alps, reinforcing the interpretation that system congestion may have prevented the transportation of critically ill patients to the emergency room on time. On the other hand, we observe that calls to the emergency line only slightly increase in the rest of Lombardy, which suggests that the system did not become congested there and helps rationalizing why more remote communities did not experience higher mortality rates there. We analyzed how emergency care congestion may have contributed to the high mortality rates observed in Lombardy during its first Covid-19 outbreak, using distance to the ICU as a proxy for access to critical care. We found that Covid-19-induced mortality was much higher in communities underserved by intensive care. Using the estimated coefficients we performed some back-of-envelope calculations to calculate how many deaths can be ascribed to system congestion. We found that more than 25% of all fatalities of Lombardy's first outbreak may have resulted from system congestion. Our results suggest that many Covid-19 deaths may have been prevented through better preparedness. Drawing a lesson from Italy's tale, governments around the world should invest in strengthening their emergency care response. They should improve pre-hospital emergency services, by clarifying the first point of contact for possible Covid-19 cases, expanding capacity to manage large volumes of calls, and improving phone triage to better prioritize care delivery. They should also invest in building ambulance capacity, and, ideally, mobilizing ICUs more evenly across the territory. All these factors are essential to help reduce mortality during new outbreaks. 14 . 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 October 29, 2020. ; https://doi.org/10.1101/2020.10.27.20221085 doi: medRxiv preprint . 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 October 29, 2020. ; https://doi.org/10.1101/2020.10.27.20221085 doi: medRxiv preprint Notes: This map characterises excess mortality rates, calculated per 100,000 inhabitants. Excess mortality rates are calculated by subtracting 2016 mortality rates to that in 2020. This method is akin to the empirical method we use for counterfactual estimation of the effects of COVID-19 on mortality (see Section ??). Lighter (darker) colors denote lower (higher) excess mortality rates associated to COVID-19. The black lines denote the four different geographical areas under which the emergency care service is organized. . 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 October 29, 2020. ; https://doi.org/10.1101/2020.10.27.20221085 doi: medRxiv preprint Figure A3 : The dynamic effects of Covid-19 on the mortality rate Notes: The figure shows the effect of Covid-19 on the mortality rate, measured in daily deaths per 100,000 inhabitants. Coefficients are estimated from Equation (1). The blue solid line show coefficients estimated using 2016 as counterfactual, while the blue shaded area depicts 95% confidence interval. The red line with crosses depicts estimates obtained using 2015-2019 mean mortality as counterfactual. Figure A4 : Distance to ICU and mortality rates Notes: The figure depicts the relationship between distance to ICU and observed mortality rates. Precisely, it plots the average daily mortality rate per 100,000 inhabitants over the February/21/2020-May/15/2020 period onto distance to ICU, measured in minutes of driving for all municipalities in Lombardy. . 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 October 29, 2020. ; https://doi.org/10.1101/2020.10.27.20221085 doi: medRxiv preprint Figure A5 : Robustness checks on baseline estimates Notes: The figure shows the additional effect of Covid-19 on the mortality rate in municipalities that are 15 minutes driving away from the closest ICU relative to the effect in municipalities with an ICU in town. The red solid line depicts our baseline estimates (15 × π t from Equation (2)). The dotted black line depicts estimates obtained augmenting Equation (2) by adding a set of control variables: share of women in working age population, share of high school graduates in working age population, mean income, share of 80-plus in population, population density, mean income, external commuting index, number of days in which PM10 is above limit, per-capita ICU beds, per-capita nursing home beds and distance to the outbreak epicenter. The long-dash line depicts estimates obtained using a different distance to ICU variable, in kilometeres rather than minutes of driving. The short-dash line depicts estimates obtained by censoring observations with outlier mortality rates or distance to the ICU. The y-axis measures daily deaths per 100,000 inhabitants, the x-axis measures days since the detection of the first community cases (denoted by a vertical maroon line). . 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 October 29, 2020. ; https://doi.org/10.1101/2020.10.27.20221085 doi: medRxiv preprint Are hospitals ready for covid's second wave? Bloomberg Opinion The countries with the most critical care beds per capita What will be the economic impact of covid-19 in the us? rough estimates of disease scenarios The macroeconomics of epidemics Optimal mitigation policies in a pandemic: Social distancing and working from home Why is covid-19 mortality in lombardy so high? evidence from the simulation of a seihcr model Raccomandazioni di etica clinica per l'ammissione e trattamenti intensivi e per la loro sospensione Decessi del 2020. dataset analitico con i decessi giornalieri Indicatori demografici. popolazione residente al 1°gennaio., 2020. Data retrieved on COVID-19 in Italy: An Analysis of Death Registry Data Can telehealth ontario respiratory call volume be used as a proxy for emergency department respiratory visit surveillance by public health? Isaia Invernizzi. Coronavirus, chiamate al 118 giù del 90% ma l'attenzione resta altainfografica, 2020. Retrieved on October 10 Letti per struttura sanitaria di ricovero, 2020. Open Data Portal 2020. Data retrieved on October 10 Business shutdowns and covid-19 mortality Un'ora di attesa per un'ambulanza. ora anche il 118 rischia il collasso Elenco rsa accreditate, 2020. Open Data Portal Condizioni socio-economiche delle famiglie Share of employment in non-essential sectors, % total employment