Demographic and Socio-Economic Factors, and Healthcare Resource Indicators Associated with the Rapid Spread of COVID-19 in Northern Italy: An Ecological Study

COVID-19; pandemic; ecological studies; SARS-CoV-2 27 . CC-BY-NC-ND 4.0 International licenseIt 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 (which was not certified by peer review) The copyright holder for this preprint this version posted April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 3

Background: COVID-19 rapidly escalated into a pandemic, threatening 213 countries, areas, and territories the world over. We aimed to identify potential province-level socioeconomic 30 determinants of the virus’s dissemination, and explain between-province differences in the speed of its spread, based on data from 36 provinces of Northern Italy.

This is an ecological study. We included all confirmed cases of SARS-CoV-2 reported 33 between February 24th and March 30th, 2020. For each province, we calculated the trend of contagion as the relative increase in the number of individuals infected between two time endpoints, assuming an exponential growth. Pearson’s test was used to correlate the trend of contagion with a 36 set of healthcare-associated, economic, and demographic parameters by province. The virus’s spread was input as a dependent variable in a stepwise OLS regression model to test the association between rate of spread and province-level indicators. 39 Findings: Multivariate analysis showed that the spread of COVID-19 was correlated negatively with aging index (p-value=0.003), and positively with public transportation per capita (p- value=0.012), the % of private long-term care hospital beds and, to a lesser extent (p-value=0.070), 42 the % of private acute care hospital beds (p-value=0.006). Interpretation: Demographic and socioeconomic factors, and healthcare organization variables were found associated with a significant difference in the rate of COVID-19 spread in 36 provinces 45 of Northern Italy. An aging population seemed to naturally contain social contacts. The availability of healthcare resources and their coordination could play an important part in spreading infection. . CC-BY-NC-ND 4.0 International licenseIt 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 (which was not certified by peer review) The copyright holder for this preprint this version posted April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 4

48 The disease caused by the SARS-CoV-2 virus (COVID-19) first detected in Wuhan, China, in December 20191 escalated rapidly into a pandemic, 2 coming to threaten 213 countries, areas, and territories across the globe.3 51 Italy recorded its first imported cases in January 2020, 4 and local cases emerged towards the end of February in Northern Italy, in the Lombardia and V eneto regions. 4,5 Restrictions on international travel, domestic mobility, mass gatherings, and sporting events were gradually enacted by the 54 Italian Government in an effort to contain the contagion,4 until the country’s complete lockdown on March 9th, 2020.6 Once implemented, these mitigation strategies (which sounded draconian to the population) proved effective in limiting the diffusion of COVID-19 to other parts of the country, but 57 the infection continued to spread with different trends in Northern Italy. Many demographic and socioeconomic factors, and healthcare organization variables can be associated with the variability of contagious disease propagation rates. It is important for public 60 health systems to identify these factors and variables to produce solid evidence that can drive mitigation strategies. As well as having a direct influence on health outcomes in a given area, the findings of public health research also have important implications for ongoing global efforts to 63 contain COVID-19. For example, a recent study found that even a small increase in long-term exposure to PM2.5 leads to a large increase in the virus-related death rate, with the magnitude of increase 20 times that observed for PM2.5 and all cause mortality.7 66 Besides environmental conditions, factors such as employment rates, population density, and healthcare resources may help to account for the observed differences in rapidity of COVID-19 propagation. 69 With this ecological study we aimed to identify potential province-level demographic, socioeconomic, and healthcare determinants of the COVID-19 pandemic’s dissemination, and to . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 5 explain between-province differences in the rate of the disease’s spread, based on data from 36 72 provinces in four regions of Northern Italy. . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 6

Context 75 The Italian National Health Service (NHS) was set up in 1978 with universal coverage, solidarity, human dignity, and human health as its guiding principles. It is regionally based, and organized at national, regional, and local levels. Under the Italian Constitution, central government controls the 78 distribution of tax revenue for publicly-financed healthcare and decides on a national statutory ben- efits package of “essential levels of care” - offered to all residents in every region. The country’s 19 regions and two autonomous provinces are responsible for organizing and delivering healthcare ser-81 vices through local health units. Depending on the region, public funds are allocated by local health units to public hospitals and accredited private clinics. In 2017, there were approximately 213,700 hospital beds, including day hospital and day surgery beds: 170,000 in public hospitals (2.8 beds per 84 1,000 population, including 2.5/1,000 for acute care and 0.3/1,000 for long-stay patients), and 43,700 in accredited private clinics (0.7 beds per 1,000 population, including 0.4/1,000 for acute care and 0.3/1,000 for long-stay patients). 8 Public hospitals are either managed directly by local 87 health units and coordinated with other local health services such as preventive medicine depart- ments and primary care districts, or they operate as semi-independent public enterprises. A prospec- tive payment system based on diagnosis-related groups (DRG) operates across the country and ac-90 counts for most hospital revenue, but is generally not applied to hospitals run directly by local health units, where global budgets are common. 93 Study design and data sources In the present ecological study we included all confirmed cases of SARS-CoV-2 reported between February 24th and March 30th, 2020, drawn from the Italian Civil Protection Department’s official 96 data.9 This agency operates certified surveillance systems for the Italian government, with a two- . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 7 stage testing procedure for SARS-CoV-2, one at local health unit level, and one at national labora- tory level for confirmation purposes. All reported cases of COVID-19 infection met the laboratory 99 criteria for confirmation established in the EU case definition procedure, which includes using RT- PCR. We collected data on 36 provinces in four regions of Northern Italy – Piemonte, Lombardia, Veneto, 102 and Emilia-Romagna – which have had the highest numbers of infections during the COVID-19 pandemic. For each province, we calculated the trend of contagion as the relative increase in the number of people infected between two time endpoints: T0 , the day when the province first reached 105 10 cases, or as at February 2nd, 2020 (when the first data on the cases reported became available, whichever came first); and T20 (20 days after T0). Demographics were extracted from the most recently updated databases on the website of the Italian Institute of Statistics (ISTA T),10 and the 108 ISTAT’s Health for All database.11 The number of sporting associations for each province was obtained from the registry of the Italian National Olympic Committee (CONI).12 Data analysis 111 We modeled the disease’s spread assuming an exponential growth with the number of infections multiplying by a certain factor each day. Assuming no recoveries during the three-week period considered, the total number of people infected at the time t, y_t can be written as: 114 y_t=y_1 (β +1)^(t-1) where y_1 is the number of cases on day 1 and β is the transmission factor. Since we know the number of cases on day 1 and day 20, we can compute β _i, the transmission 117 factor, for the i-th province as: β _i=(y_t⁄y_1 )^(1/(t-1) )-1 A descriptive analysis was conducted, calculating minimum, maximum, and mean values, to 120 illustrate the spread of the disease. Pearson’s test was applied to correlate β _i with a set of healthcare-associated, economic, and demographic parameters at province level (see Table 1). . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 8 β _i was input as a dependent variable in an OLS regression model to study the association between 123 the rate of spread of COVID-19 and the variables that correlated with the dependent variable on a bivariate level with a p-value of less than 0.20. A stepwise selection procedure was used. A residuals analysis was performed to check the assumptions of the model. This analysis included testing for 126 the residuals’ normal distribution (Shapiro-Wilk normality test) and independence (Durbin-Watson test). A sensitivity analysis was conducted, considering different dependent variables as rates (t1/t0) or 129 incremental rates ([t1-t0]/t0), which produced the same results (data not shown). All data obtained were encoded in a master sheet using a Microsoft Office Excel spreadsheet (V ersion 2016, Microsoft Corporation, Redmond, W A). Data analysis was done with Stata 14 statistical software 132 (Stata Corp LP, College Station, TX) and R Studio Integrated Development for R (RStudio, Inc., Boston, MA). 135 . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 9

Table 2 shows that the increase in the numbers of cases in 20 days varied considerably among the 138 various provinces from a minimum of 115 cases in the province of Rovigo to a maximum of 3398 cases in Bergamo, with an average growth of 852 cases per province in 20 days. The rate of this increase was lowest in the province of Asti (4.5%) and highest in Brescia (330%), with an average 141 over all provinces of 59.9%. The scatter plot (Figure 1) shows significant correlations between the increase in transmission and several demographic, socioeconomic, and healthcare variables. A significant negative correlation 144 emerged between increment β and population aging rates, while a significant positive correlation was found with employment rates, public transportation rates, in-house density, population density, and the proportions of private acute and long-term care beds in clinics and nursing homes. 147 Table 3 shows the bivariate and multivariate analyses, demonstrating that the spread of COVID-19 in the regional setting considered was negatively associated (p-value=0.003) with the population aging index, i.e. the ratio of the number of people aged 65 or more to the number of people aged 0-150 14 years multiplied by 100. It was positively associated with: public transportation per capita, i.e. the ratio of the yearly number of public transport passengers to the total population (p- value=0.012). It was also positively associated, albeit to a lesser extent (p-value=0.070), with the % 153 of private long-term care hospital beds, and more strongly with the % of private acute care hospital beds (p-value=0.006). The Shapiro-Wilk test showed that the residuals had a normal distribution (p-value=0.870) and the 156 Rubin-Watson test showed that the residuals were independent (p-value=0.48). The high value of the adjusted R squared indicates that the model had a good fit. . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 10

159 With this ecological study, we found correlations between higher SARS-CoV-2 contagion rates and certain demographic, socioeconomic, and healthcare factors in the Italian provinces considered. Unlike the first cases imported from outbreak regions, the local spread of communicable respiratory 162 diseases seems to favor certain patterns and conditions, especially relating to social contact patterns.13 We found a negative association between COVID-19 contagion rates and aging. This seems 165 somewhat at odds with the currently-known epidemiology of the virus, that in Italy is more likely to infect and kill older people. The median age of people infected with SARS-CoV-2 nationwide is 62 years old, and the median age of the related deaths is 80 years old. 14 On the other hand, provinces 168 with a higher aging index had a more gentle contagion curve. A possible explanation is that older people tend to move less outside their home and travel less far from where they live. 15 They are also less likely to take part in mass gatherings or social events, whereas younger people tend to spend 171 more time with others outside their family.16 Taken together, these behavioral factors could predispose the elderly to social distancing and self-isolation, naturally containing the propagation of communicable diseases in the provinces with the oldest populations. 174 In our analysis, higher levels of employment, public transportation usage, in-house density, and population density correlated positively with the spread of infection. What these socioeconomic factors have in common is the mobility of individuals and their exposure to close social contacts, 177 both of which facilitate the propagation of SARS-CoV-2. This is consistent with recent analyses that associated a higher risk of COVID-19 transmission in mainland China with the use of trains,17 buses, and flights from Wuhan, and the risk was higher the longer a journey lasted.18 In-house 180 density has also already been identified as a factor predisposing to higher transmission rates of pandemic influenza in Great Britain.19 . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 11 A greater spread of the virus was seen in provinces with larger proportions of private acute care 183 beds in clinics, and long-term care beds in nursing homes. These data should not be interpreted as a lack of involvement of the private sector in providing hospital care for COVID-19 patients. These facilities are an important part of the services provided by the Italian health system and they 186 responded promptly to the emergency situation. They have ordinary and intensive care beds for COVID-19 patients just like public hospitals. The explanation should be sought by looking at the issue more in depth and speculating on the outcome variable analyzed in the present study, i.e. the 189 rate of spread of the infection. Hospitals, clinics, and care homes are only responsible for containing it within their walls, while the preventive medicine departments of local health units are in charge of wider containment efforts, based on epidemiological intelligence.20 It may be that provinces with 192 higher proportions of private healthcare facilities experienced more communication difficulties with the reporting of cases to preventive medicine departments to gather epidemic intelligence because private facilities are usually less integrated with preventive medicine and community healthcare 195 services than public hospitals. This could hamper the early identification of patients through the sort of epidemic intelligence activities strongly recommended by the WHO in the case of COVID-19. 21 Another explanation could relate to how private clinics are funded, i.e. based on DRG-based tariffs 198 for each patient admitted. Owing to incentives linked to this particular funding mechanism, provinces with a relatively high concentration of private hospital beds might have paid less attention to avoiding the hospitalization of patients with COVID-19 symptoms who could have been 201 successfully treated at home with the involvement of community and primary care services. In fact, primary healthcare services have a crucial role in ensuring that people caring for a family member suffering from COVID-19 manage their contact with the patient appropriately, and follow national 204 or local policies regarding home quarantine. Caregivers should wear medical masks or the best available protection against respiratory droplets when in close contact with the patient, and observe hand hygiene recommendations. The WHO emphasized the importance of notifying healthcare 207 . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 12 providers of the diagnosis in order to receive instructions on where to seek care, when and where to enter healthcare facilities, and what precautions to take to prevent and control the infection.21 Our findings would suggest the importance of improving communications between private clinics and 210 the Italian NHS responsible for their accreditation (i.e. the issue of the public license to provide hospital care on the NHS’s behalf) to ensure a real consistency of their activities with regional health plans (including their integration with the preventive medicine and primary care services of 213 local health units), and an effective assessment of their activities and results. This study has at least two important limitations. First, we collected data from available health indicators, which do not measure all the phenomena relevant to understanding and explaining the 216 findings, even though our models performed well in explaining their variability (MR2 = ~60%). Second, the real number of SARS-CoV-2 contagions is known to be underestimated because not all individuals in the population considered were screened, and swabs were handled differently by the 219 various regional healthcare systems. Even with such well-known limitations,22 ecological studies can still help healthcare workers and stakeholders to contain infections and fight pandemics, especially in the early stages of emerging diseases when clinical data are still limited. 23 222

Several demographic, socioeconomic, and healthcare factors were found associated with significant 225 differences in the rate of COVID-19 spread in 36 provinces of Northern Italy. An older population seemed to naturally slow the contagion due to fewer social contacts. Socioeconomic factors (especially the rates of employment and public transportation usage) and organizational features of 228 local healthcare systems (particularly the proportion of private healthcare facilities) also seemed to play an important part in the infection’s spread. These correlations could be helpful for the purpose of designing measures to reduce the propagation of COVID-19 in other countries now dealing with 231 the first phase of this pandemic. . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 13

1 World Health Organization. Disease Outbreak News Update – January 12, 2020: Novel 234 Coronavirus – China. Available at: https://www.who.int/csr/don/12-january-2020-novel- coronavirus-china/en/ Last accessed, April 13, 2020. 2 World Health Organization. WHO Director-General's opening remarks at the media briefing 237 on COVID-19 - 11 March 2020. Available at: https://www.who.int/dg/speeches/detail/who- director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 Last accessed, April 13, 2020. 240 3 World Health Organization. Coronavirus disease (COVID-19) Pandemic – April 12 Update. Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 Last accessed April 13, 2020. 243 4 Italian Ministry of Health. COVID-19 – Italian Situation. 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(which was not certified by peer review) The copyright holder for this preprint this version posted April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 303 Table 1: Environmental, health, economic, and population factors by province Provinc- es Region Resident popula- tion Aging index Popula- tion density % of sports associa- tions/resident popu- lation Unemploy- ment rate Employ- ment rate % of active com- panies/resident population Local public transpor t usage per capi- ta PD Veneto 937908 169.6 429.7 0.186 5.84 52.36 9.0 133.1 BG Lombardi a 1114590 145.2 395.6 0.157 4.86 50.82 7.8 271.9 PV Lombardi a 545888 197.8 180.5 0.137 6.49 49.58 7.1 100.7 CR Lombardi a 358955 188.9 202 0.159 5.1 50.16 6.9 18.1 LO Lombardi a 230198 153.8 285.76 0.156 6.51 51.33 6.2 20.6 PI Emilia Romagna 287152 195.9 110.07 0.157 5.59 51.54 8.2 70.1 TV Veneto 887806 156.9 353.57 0.185 8.01 51.33 8.1 96.5 . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint VE Veneto 853338 198.2 342.5 0.166 6.4 47.83 7.7 655.7 MI Lombardi a 3250315 166.9 1928.4 0.115 6.45 52.79 9.1 689.2 BR Lombardi a 1265954 151.2 258.7 0.165 5.16 51.72 8.2 167.7 PR Emilia Romagna 451631 175.1 123.98 0.164 4.78 53.9 8.4 141.8 MO Emilia Romagna 705393 164 255.13 0.142 6.04 52.68 7.5 43.9 TO Piemonte 2244488 198.1 6 329.25 0.147 9.22 48.31 33.3 230.4 RI Emilia Romagna 339017 172.7 372.03 0.183 8.23 52.56 10.2 95.3 AT Piemonte 223018 212.1 3 144.07 0.146 8.38 46.88 23.6 43.6 VR Veneto 926497 157.6 290.83 0.164 7.04 51.8 8.1 148.7 MN Lombardi 412292 177.9 174.39 0.155 6.68 50.2 7.2 78.5 . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint a RE Emilia Romagna 531891 149.6 225.78 0.164 4.21 53.26 7.8 64.9 VI Veneto 862418 158.6 315.59 0.175 5.26 51.73 8.2 50.8 VA Lombardi a 890768 174.6 727.71 0.161 5.93 50.7 7.4 71.9 MB Lombardi a 873935 160.9 2072.09 0.124 6.04 51.61 7.7 38.9 BO Emilia Romagna 1014619 190.1 263.68 0.167 5.64 53.39 8.6 246.4 AL Piemonte 434050 245.7 9 120.05 0.166 10.03 46.21 26.6 36.5 CO Lombardi a 599204 169.3 458.72 0.145 7.31 50.99 7.6 109.9 LC Lombardi a 337380 174.8 412.86 0.145 5.63 51.48 7.6 61.7 BL Veneto 202950 228.3 57.19 0.237 4.35 51.03 7.4 67 . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint RA Emilia Romagna 389456 201.4 206.92 0.205 5.85 50.84 8.0 39.9 VC Piemonte 180272 236.2 0 85 0.173 7.18 47.7 23.5 12.9 VB Piemonte 164647 233.7 9 70.88 0.183 5.68 46.57 21.2 46.9 FC Emilia Romagna 394627 184 164.29 0.192 4.83 51.92 8.9 57.6 BI Piemonte 184695 263.2 7 199.49 0.144 6.47 48.81 28.4 22.9 NO Piemonte 367939 181.1 0 272.75 0.163 8.5 48.23 27.3 84.0 CN Piemonte 594729 179.4 6 85.04 0.173 4.35 51.85 31.3 63.8 RO Veneto 287152 234.3 133.21 0.161 6.85 47.4 6.9 11.4 SO Lombardi a 181095 181.4 56.58 0.149 6.65 49.11 7.5 3.6 . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint FE Emilia Romagna 345691 255.5 134.14 0.166 9.06 47.57 7.6 54.1 PD: Padova; BG: Bergamo; PV: Pavia; CR: Cremona; LO: Lodi; PI: Piacenza; TV: Treviso; VE: Venezia; MI: Milano; BR: Brescia; PR: Parma; MO: Modena; TO: Torino; RI: Rimini; AT: Asti; VR: Verona; MN: Mantova; RE: Reggio Emilia; VI: Vicenza; V A: Varese; MB: Monza Brianza; BO: 306 Bologna; AL: Alessandria; CO: Como; LC: Lecco; BL: Belluno; RA: Ravenna; VC: Vercelli; VB: Verbano-Cuso-Ossola; FC: Forlì-Cesena; BI: Biella; NO: Novara; CN: Cuneo; RO: Rovigo; SO: Sondrio; FE: Ferrara . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint 309 Table 2: Spread of COVID-19 (difference in cases, rate increase, incremental rate increase, and transmission rate β by province Provinces T0 Number of cases at T0 T1 Number of cases at T1 Difference in cases Rate of increase in cases (T1/T0) Incremental rate (T1- T0)/T1 β (see Methods for calculation) PD 25/02 30 15/03 658 628 21.9 20.9 0.176 BG 25/02 18 15/03 3416 3398 189.8 188.8 0.318 PV 25/02 27 15/03 722 695 26.7 25.7 0.189 CR 25/02 53 15/03 1792 1739 33.8 32.8 0.204 LO 25/02 125 15/03 1320 1195 10.6 9.6 0.132 PI 25/02 18 15/03 1012 994 56.2 55.2 0.236 TV 27/02 22 17/03 502 480 22.8 21.8 0.179 VE 27/02 14 17/03 378 364 27.0 26.0 0.189 MI 27/02 15 17/03 2326 2311 155.1 154.1 0.304 BR 27/02 10 17/03 3300 3290 330.0 329.0 0.357 . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint PR 27/02 10 17/03 800 790 80.0 79.0 0.259 MO 27/02 18 17/03 460 442 25.6 24.6 0.186 TO 28/02 11 17/03 749 738 68.1 67.1 0.249 RI 29/02 15 19/03 691 676 46.1 45.1 0.223 AT 02/03 37 21/03 166 129 4.5 3.5 0.082 VR 03/03 17 22/03 1046 1029 61.5 60.5 0.242 MN 03/03 15 22/03 905 890 60.3 59.3 0.241 RE 03/03 14 22/03 1167 1153 83.4 82.4 0.262 VI 04/03 10 23/03 691 681 69.1 68.1 0.25 VA 04/03 11 23/03 421 410 38.3 37.3 0.211 MB 04/03 11 23/03 1130 1119 102.7 101.7 0.276 BO 04/03 11 23/03 833 822 75.7 74.7 0.256 AL 04/03 16 23/03 817 801 51.1 50.1 0.23 CO 05/03 11 24/03 635 624 57.7 56.7 0.238 LC 06/03 11 25/03 1076 1065 97.8 96.8 0.273 BL 07/03 11 26/03 313 302 28.5 27.5 0.193 RA 07/03 10 26/03 451 441 45.1 44.1 0.222 . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint VC 07/03 10 26/03 336 326 33.6 32.6 0.203 VB 07/03 10 26/03 255 245 25.5 24.5 0.186 FC 08/03 15 27/03 580 565 38.7 37.7 0.212 BI 08/03 19 27/03 367 348 19.3 18.3 0.169 NO 08/03 13 27/03 609 596 46.8 45.8 0.224 CN 09/03 11 28/03 558 547 50.7 49.7 0.23 RO 10/03 10 29/03 125 115 12.5 11.5 0.142 SO 11/03 13 30/03 446 433 34.3 33.3 0.205 FE 11/03 12 30/03 306 294 25.5 24.5 0.186 MIN 10 125 115 4.5 11.5 0.082 Max 125 3416 3398 330 27.184 0.357 Mean 19 871.1 852.1 59.9 58.9 0.221 The increase in the numbers of cases in 20 days varied considerably among the various provinces. T0 is the date of first details or 25.2. or report of 10th case. T1: 20 days after T0. PD: Padova; BG: Bergamo; PV: Pavia; CR: Cremona; LO: Lodi; PI: Piacenza; TV: Treviso; VE: Venezia; MI: Milano; BR: Brescia; PR: Parma; MO: Modena; TO: Torino; RI: Rimini; AT: Asti; VR: Verona; MN: 312 Mantova; RE: Reggio Emilia; VI: Vicenza; V A: V arese; MB: Monza Brianza; BO: Bologna; AL: Alessandria; CO: Como; LC: Lecco; BL: Belluno; RA: Ravenna; VC: Vercelli; VB: Verbano-Cuso-Ossola; FC: Forlì-Cesena; BI: Biella; NO: Novara; CN: Cuneo; RO: Rovigo; SO: Sondrio; FE: Ferrara. 315 . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint Table 3: Association of covariates with COVID-19 transmission factor Variable Pearson’s rho (p-value) Full model coefficients (p- value) Final model coefficients (p- value) Intercept (0,934) --- 0.229 (<0.001) 0.220 (<0.001) Aging index -0.447 (0.006) -0.013 (0.336) -0.021 (0.003) Population density 0.312 (0.064) 0.001 (0.880) --- % sports associations / resident population -0.206 (0.227) --- --- Unemployment rate -0.274 (0.106) -0.001 (0.934) --- Employment rate 0.336 (0.045) 0.010 (0.546) --- % of active companies / resident population -0.154 (0.368) --- --- Local public transport use per capita 0.338 (0.044) 0.016 (0.042) 0.016 (0.012) % of private long-term care beds 0.350 (0.036) 0.010 (0.324) 0.014 (0.070) % of private acute care hospital beds 0.486 (0.003) 0.022 (0.022) 0.021 (0.006) % of anesthesiologists / resident population -0.083 (0.689) --- --- General practitioners rate -0.164 (0.369) --- --- Dentists rate 0.201 (0.239) --- --- . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint Nursing staff rate 0.071 (0.681) --- --- Acute care hospital beds rate 0.092 (0.593) --- --- Long-term care hospital beds rate -0.052 (0.761) --- --- Emergency medical service rate -0.187 (0.306) --- --- Region: Veneto vs Lombardia --- -0.017 (0.458) --- Region: Emilia Romagna vs Lombardia --- -0.022 (0.385) --- Region: Piemonte vs Lombardia --- 0.000 (1.000) --- Model parameters Multiple R2=0.629 Adjusted R2=0.475 AIC=-118.35 BIC=-99.69 Multiple R2=0.604 Adjusted R2=0.551 AIC=-128.01 BIC=-118.67 The spread of COVID-19 in the regional setting considered is associated with the population aging index and positively associated with public 318 transportation per capita, % of private long-term care hospital beds, % of private acute care hospital beds. . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint Figure 1: Significant correlations between β increment and ·socioeconomic and healthcare variables The spread of COVID-19 was correlated negatively with aging index and positively with the other indicators showed. . CC-BY-NC-ND 4.0 International licenseIt 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 April 29, 2020. ; https://doi.org/10.1101/2020.04.25.20078311doi: medRxiv preprint