A novel, integrated approach for understanding and investigating Healthcare Associated Infections: A risk factors constellation analysis.

INTRODUCTION: Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) are major public health threats in upper- and lower-middle-income countries. Electronic health records (EHRs) are an invaluable source of data for achieving different goals, including the early detection of HAIs and AMR clusters within healthcare settings; evaluation of attributable incidence, mortality, and disability-adjusted life years (DALYs); and implementation of governance policies. In Italy, the burden of HAIs is estimated to be 702.53 DALYs per 100,000 population, which has the same magnitude as the burden of ischemic heart disease. However, data in EHRs are usually not homogeneous, not properly linked and engineered, or not easily compared with other data. Moreover, without a proper epidemiological approach, the relevant information may not be detected. In this retrospective observational study, we established and engineered a new management system on the basis of the integration of microbiology laboratory data from the university hospital "Policlinico Tor Vergata" (PTV) in Italy with hospital discharge forms (HDFs) and clinical record data. All data are currently available in separate EHRs. We propose an original approach for monitoring alert microorganisms and for consequently estimating HAIs for the entire period of 2018. METHODS: Data extraction was performed by analyzing HDFs in the databases of the Hospital Information System. Data were compiled using the AREAS-ADT information system and ICD-9-CM codes. Quantitative and qualitative variables and diagnostic-related groups were produced by processing the resulting integrated databases. The results of research requests for HAI microorganisms and AMR profiles sent by the departments of PTV from 01/01/2018 to 31/12/2018 and the date of collection were extracted from the database of the Complex Operational Unit of Microbiology and then integrated. RESULTS: We were able to provide a complete and richly detailed profile of the estimated HAIs and to correlate them with the information contained in the HDFs and those available from the microbiology laboratory. We also identified the infection profile of the investigated hospital and estimated the distribution of coinfections by two or more microorganisms of concern. Our data were consistent with those in the literature, particularly the increase in mortality, length of stay, and risk of death associated with infections with Staphylococcus spp, Pseudomonas aeruginosa, Klebsiella pneumoniae, Clostridioides difficile, Candida spp., and Acinetobacter baumannii. Even though less than 10% of the detected HAIs showed at least one infection caused by an antimicrobial resistant bacterium, the contribution of AMR to the overall risk of increased mortality was extremely high. CONCLUSIONS: The increasing availability of health data stored in EHRs represents a unique opportunity for the accurate identification of any factor that contributes to the diffusion of HAIs and AMR and for the prompt implementation of effective corrective measures. That said, artificial intelligence might be the future of health data analysis because it may allow for the early identification of patients who are more exposed to the risk of HAIs and for a more efficient monitoring of HAI sources and outbreaks. However, challenges concerning codification, integration, and standardization of health data recording and analysis still need to be addressed.

Characteristics of nursing homes and early preventive measures associated with risk of infection from COVID-19 in Lazio region, Italy: a retrospective case-control study.

OBJECTIVES: To understand which organisational-structural characteristics of nursing homes-also referred to as long-term care facilities (LTCFs)-and the preventative measures adopted in response to the pandemic are associated with the risk of a COVID-19 outbreak. SETTING: LTCFs in Lazio region in Italy. DESIGN: The study adopts a case-control design. PARTICIPANTS: We included 141 facilities and 100 provided information for the study. Cases were defined as facilities reporting a COVID-19 outbreak (two or more cases) in March-December 2020; controls were defined as LTCFs reporting one case or zero. The exposures include the structural-organisational characteristics of the LTCFs as reported by the facilities, preventative measures employed and relevant external factors. RESULTS: Twenty facilities reported an outbreak of COVID-19. In binary logistic regression models, facilities with more than 15 beds were five times more likely to experience an outbreak than facilities with less than 15 beds OR=5.60 (CI 1.61 to 25.12; p value 0.002); admitting new residents to facilities was associated with a substantially higher risk of an outbreak: 6.46 (CI 1.58 to 27.58, p value 0.004). In a multivariable analysis, facility size was the only variable that was significantly associated with a COVID-19 outbreak OR= 5.37 (CI 1.58 to 22.8; p value 0.012) for larger facilities (>15 beds) versus smaller (<15 beds). Other characteristics and measures were not associated with an outbreak. CONCLUSION: There was evidence of a higher risk of COVID-19 in larger facilities and when new patients were admitted during the pandemic. All other structural-organisational characteristics and preventative measures were not associated with an outbreak. This finding calls into question existing policies, especially where there is a risk of harm to residents. One such example is the restriction of visitor access to facilities, resulting in the social isolation of residents.

Emergency Medical Services Calls Analysis for Trend Prediction during Epidemic Outbreaks: Interrupted Time Series Analysis on 2020-2021 COVID-19 Epidemic in Lazio, Italy.

(1) Background: During the COVID-19 outbreak in the Lazio region, a surge in emergency medical service (EMS) calls has been observed. The objective of present study is to investigate if there is any correlation between the variation in numbers of daily EMS calls, and the short-term evolution of the epidemic wave. (2) Methods: Data from the COVID-19 outbreak has been retrieved in order to draw the epidemic curve in the Lazio region. Data from EMS calls has been used in order to determine Excess of Calls (ExCa) in the 2020-2021 years, compared to the year 2019 (baseline). Multiple linear regression models have been run between ExCa and the first-order derivative (D') of the epidemic wave in time, each regression model anticipating the epidemic progression (up to 14 days), in order to probe a correlation between the variables. (3) Results: EMS calls variation from baseline is correlated with the slope of the curve of ICU admissions, with the most fitting value found at 7 days (R2 0.33, p < 0.001). (4) Conclusions: EMS calls deviation from baseline allows public health services to predict short-term epidemic trends in COVID-19 outbreaks, and can be used as validation of current data, or as an independent estimator of future trends.

Pro-active monitoring and social interventions at community level mitigate the impact of coronavirus (COVID-19) epidemic on older adults' mortality in Italy: A retrospective cohort analysis.

BACKGROUND: The COVID-19 epidemic in Italy has severely affected people aged more than 80, especially socially isolated. Aim of this paper is to assess whether a social and health program reduced mortality associated to the epidemic. METHODS: An observational retrospective cohort analysis of deaths recorded among >80 years in three Italian cities has been carried out to compare death rate of the general population and "Long Live the Elderly!" (LLE) program. Parametric and non-parametric tests have been performed to assess differences of means between the two populations. A multivariable analysis to assess the impact of covariates on weekly mortality has been carried out by setting up a linear mixed model. RESULTS: The total number of services delivered to the LLE population (including phone calls and home visits) was 34,528, 1 every 20 day per person on average, one every 15 days during March and April. From January to April 2019, the same population received one service every 41 days on average, without differences between January-February and March-April. The January-April 2020 cumulative crude death rate was 34.8‰ (9,718 deaths out of 279,249 individuals; CI95%: 34.1-35.5) and 28.9‰ (166 deaths out of 5,727 individuals; CI95%:24.7-33.7) for the general population and the LLE sample respectively. The general population weekly death rate increased after the 11th calendar week that was not the case among the LLE program participants (p<0.001). The Standardized Mortality Ratio was 0.83; (CI95%: 0.71-0.97). Mortality adjusted for age, gender, COVID-19 weekly incidence and prevalence of people living in nursing homes was lower in the LLE program than in the general population (p<0.001). CONCLUSIONS: LLE program is likely to limit mortality associated with COVID-19. Further studies are needed to establish whether it is due to the impact of social care that allows a better clients' adherence to the recommendations of physical distancing or to an improved surveillance of older adults that prevents negative outcomes associated with COVID-19.

The Effectiveness of Intervening on Social Isolation to Reduce Mortality during Heat Waves in Aged Population: A Retrospective Ecological Study.

BACKGROUND: Heat waves are correlated with increased mortality in the aged population. Social isolation is known as a vulnerability factor. This study aims at evaluating the correlation between an intervention to reduce social isolation and the increase in mortality in the population over 80 during heat waves. METHODS: This study adopted a retrospective ecologic design. We compared the excess mortality rate (EMR) in the over-80 population during heat waves in urban areas of Rome (Italy) where a program to reduce social isolation was implemented, to others where it was not implemented. We measured the mortality of the summer periods from 2015 to 2019 compared with 2014 (a year without heat waves). Winter mortality, cadastral income, and the proportion of people over 90 were included in the multivariate Poisson regression. RESULTS: The EMR in the intervention and controls was 2.70% and 3.81%, respectively. The rate ratio was 0.70 (c.i. 0.54-0.92, p-value 0.01). The incidence rate ratio (IRR) of the interventions, with respect to the controls, was 0.76 (c.i. 0.59-0.98). After adjusting for other variables, the IRR was 0.44 (c.i. 0.32-0.60). CONCLUSIONS: Reducing social isolation could limit the impact of heat waves on the mortality of the elderly population.

Predicting SARS-CoV-2 Weather-Induced Seasonal Virulence from Atmospheric Air Enthalpy.

Following the coronavirus disease 2019 (COVID-19) pandemic, several studies have examined the possibility of correlating the virulence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, to the climatic conditions of the involved sites; however, inconclusive results have been generally obtained. Although neither air temperature nor humidity can be independently correlated with virus viability, a strong relationship between SARS-CoV-2 virulence and the specific enthalpy of moist air appears to exist, as confirmed by extensive data analysis. Given this framework, the present study involves a detailed investigation based on the first 20-30 days of the epidemic before public health interventions in 30 selected Italian provinces with rather different climates, here assumed as being representative of what happened in the country from North to South, of the relationship between COVID-19 distributions and the climatic conditions recorded at each site before the pandemic outbreak. Accordingly, a correlating equation between the incidence rate at the early stage of the epidemic and the foregoing average specific enthalpy of atmospheric air was developed, and an enthalpy-based seasonal virulence risk scale was proposed to predict the potential danger of COVID-19 outbreak due to the persistence of weather conditions favorable to SARS-CoV-2 viability. As an early detection tool, an unambiguous risk chart expressed in terms of coupled temperatures and relative humidity (RH) values was provided, showing that safer conditions occur in the case of higher RHs at the highest temperatures, and of lower RHs at the lowest temperatures. Despite the complex determinism and dynamics of the pandemic and the related caveats, the restriction of the study to its early stage allowed the proposed risk scale to result in agreement with the available infectivity data highlighted in the literature for a number of cities around the world.

Delay in Breast Cancer Treatments During the First COVID-19 Lockdown. A Multicentric Analysis of 432 Patients.

BACKGROUND/AIM: Extraordinary restrictions aimed to limit Sars-CoV-2 spreading; they imposed a total reorganization of the health-system. Oncological treatments experienced a significant slowdown. The aim of our multicentric retrospective study was to evaluate screening suspension and surgical treatment delay during COVID-19 and the impact on breast cancer presentation. PATIENTS AND METHODS: All patients who underwent breast surgery from March 11, 2020 to May 30, 2020 were evaluated and considered as the Lockdown group. These patients were compared with similar patients of the previous year, the Pre-Lockdown group. RESULTS: A total of 432 patients were evaluated; n=223 and n=209 in the Lockdown and Pre-lockdown-groups, respectively. At univariate analysis, waiting times, lymph-nodes involvement and cancer grading, showed a statistically significant difference (p<0.05). Multivariate analysis identified waiting-time on list (OR=1.07) as a statistically significant predictive factor of lymph node involvement. CONCLUSION: Although we did not observe a clinically evident difference in breast cancer presentation, we reported an increase in lymph node involvement.

Lockdown of Breast Cancer Screening for COVID-19: Possible Scenario.

BACKGROUND/AIM: Coronavirus disease is spreading worldwide. Due to fast transmission and high fatality rate drastic emergency restrictions were issued. During the lockdown, only urgent medical services are guaranteed. All non-urgent services, as breast cancer (BC) screening, are temporarily suspended. The potential of breast cancer screening programs in increasing the survival rate and decreasing the mortality rate has been widely confirmed. Suspension could lead to worse outcomes for breast cancer patients. Our study aimed to analyse the data and provide estimates regarding the temporary BC screening suspension. PATIENTS AND METHODS: Data regarding breast cancer and respective screening programs were achieved through literature research and analysis. RESULTS: Considering three different scenarios with respect to the lockdown's impact on breast cancer screening, we estimate that approximately 10,000 patients could have a missed diagnosis during these 3 months. Considering a 6-month period, as suggested by the Imperial college model, the number of patients who will not receive a diagnosis will rise to 16,000. CONCLUSION: Breast cancer screening should be resumed as soon as possible in order to avoid further breast cancer missed diagnosis and reduce the impact of delayed diagnosis.

Effect of Lockdown in Surgical Emergency Accesses: Experience of a COVID-19 Hospital.

BACKGROUND/AIM: SARS-CoV-2 pandemic imposed extraordinary restriction measures and a complete reorganization of the Health System. The aim of the study was to evaluate the impact of COVID-19 on emergency surgical department accesses. PATIENTS AND METHODS: Patients admitted to surgical emergency departments was retrospectively recorded during the Lockdown (March 11, 2020-May 3, 2020) and compared with the same number of days in 2019 and immediately before Lockdown (January 16, 2020-March 10, 2020). Diagnoses, priority levels, modes of patient's transportation, waiting times and outcomes were analysed. RESULTS: During the lockdown phase, we ob-served a reduction in the access to emergency surgical departments of 84.45% and 79.78%, com-pared with the Pre-Lockdown2019 and Pre-Lockdown2020 groups, respectively. Patient's transportation, hospitalization and patients discharge with indications to an outpatient visit, waiting and total times exhibited a significant difference during the lockdown (p<0.005). CONCLUSION: We observed a reduction of surgical emergency accesses during the lockdown. Implementing the use of the regional systems and preventing overcrowding of emergency departments could be beneficial for reducing waiting times and improving the quality of treatments for patients.

On the Optimal Indoor Air Conditions for SARS-CoV-2 Inactivation. An Enthalpy-Based Approach.

In the CoViD-19 pandemic, the precautionary approach suggests that all possible measures should be established and implemented to avoid contagion, including through aerosols. For indoor spaces, the virulence of SARS-CoV-2 could be mitigated not only via air changes, but also by heating, ventilation, and air conditioning (HVAC) systems maintaining thermodynamic conditions possibly adverse to the virus. However, data available in literature on virus survival were never treated aiming to this. In fact, based on comparisons in terms of specific enthalpy, a domain of indoor comfort conditions between 50 and 60 kJ/kg is found to comply with this objective, and an easy-to-use relationship for setting viable pairs of humidity and temperature using a proper HVAC plant is proposed. If confirmed via further investigations on this research path, these findings could open interesting scenarios on the use of indoor spaces during the pandemic.