Risk of SARS-CoV-2 infection, hospitalization and death for COVID-19 in people with Parkinson's disease or parkinsonism over a 15-month period: a cohort study.

BACKGROUND: The patterns of long term risk of SARS-CoV-2 infection, hospitalization for COVID-19 and related death are uncertain in people with Parkinson's disease (PD) or parkinsonism (PS). The aim of the study was to quantify these risks compared to a control population cohort, during the period March 2020-May 2021, in Bologna, northern Italy. METHOD: ParkLink Bologna cohort (759 PD; 192 PS) and controls (9,226) anonymously matched (ratio 1:10) for sex, age, district, comorbidity were included. Data were analysed in the whole period and in the two different pandemic waves (March-May 2020 and October 2020-May 2021). RESULTS: Adjusted hazard ratio of SARS-CoV-2 infection was 1.3 (95% CI 1.04-1.7) in PD and 1.9 (1.3-2.8) in PS compared to the controls. The trend was detected in both the pandemic waves. Adjusted hazard ratio of hospitalization for COVID-19 was 1.1 (95% CI 0.8-1.7) in PD and 1.8 (95% CI 0.97-3.1) in PS. A higher risk of hospital admission was detected in PS only in the first wave. The 30-day mortality risk after hospitalization was higher (p=0.048) in PS (58%) than in PD (19%) and controls (26%). CONCLUSIONS: Compared with controls, after adjustment for key covariates, people with PD and PS showed a higher risk of SARS-CoV-2 infection throughout the first 15 months of the pandemic. COVID-19 hospitalization risk was increased only in people with PS and only during the first wave. This group of patients was burdened by a very high risk of death after infection and hospitalization.

The Indirect Impact of COVID-19 on Major Clinical Outcomes of People With Parkinson's Disease or Parkinsonism: A Cohort Study.

Background: The indirect impact of the COVID-19 epidemic on major clinical outcomes of people with Parkinson's disease (PD) or other parkinsonism is unknown. Objectives: The study aimed to (1) describe changes in healthcare services during the first epidemic bout in people with PD or parkinsonism; (2) compare the occurrence of hospitalization for any PD-related major clinical outcomes in 2020 with 2019; (3) investigate the factors, including changes in healthcare services, associated with major clinical outcomes and death. Methods: All healthcare services of the province of Bologna and major clinical outcomes were assessed through a record linkage study (ParkLink Bologna) using clinical data and health databases. Same analyses were performed in a random cohort of controls matched for age, sex, district of residence, and comorbidities with the ParkLink cohort (ratio of 1:10). Results: A cohort of subjects with PD (759) or other parkinsonism (192) was included together with a cohort of controls (9,226). All indicators of healthcare services dropped at least below 50% during the lockdown period in all cohorts, mostly impacting physiotherapy in people with PD (-93%, 95% CI 88-96%). In 2020, compared to 2019, a three-fold risk of major injuries (RR 3.0, 95% CI 1.5-6.2) and infections (RR 3.3, 95% CI 1.5-7.2), excluding COVID-19, was observed only in people with PD, and neither in people with parkinsonism nor in controls. Decreased physiotherapy was associated with the occurrence of at least one major clinical outcome (OR 3.3, 95% CI 1.1-9.8) in people with PD. Experiencing at least one major clinical outcome was the strongest risk factor for death (OR 30.4, 95% CI 11.1-83.4) in people with PD. Conclusions: During the first COVID-19 epidemic peak, healthcare services were drastically reduced in a province of northern Italy, regardless of the disease condition. However, compared to 2019, in 2020, only people with PD had a higher risk of major clinical outcomes, that were associated with higher mortality. Strategies to maintain physical activity in people with PD should be implemented in possible future health emergencies.

Risk of Hospitalization and Death for COVID-19 in People with Parkinson's Disease or Parkinsonism.

BACKGROUND: The risk of COVID-19 and related death in people with Parkinson's disease or parkinsonism is uncertain. The aim of the study was to assess the risk of hospitalization for COVID-19 and death in a cohort of patients with Parkinson's disease or parkinsonism compared with a control population cohort, during the epidemic bout (March-May 2020) in Bologna, northern Italy. METHODS: Participants of the ParkLink study with the clinical diagnosis of Parkinson's disease or parkinsonism and people anonymously matched (ratio 1:10) for sex, age, district, and Charlson Index were included. The hospital admission rate for COVID-19 (February 26-May 31, 2020) and the death rate for any cause were the outcomes of interest. RESULTS: The ParkLink cohort included 696 subjects with Parkinson's disease and 184 with parkinsonism, and the control cohort had 8590 subjects. The 3-month hospitalization rate for COVID-19 was 0.6% in Parkinson's disease, 3.3% in parkinsonism, and 0.7% in controls. The adjusted hazard ratio (age, sex, district, Charlson Index) was 0.8 (95% CI, 0.3-2.3, P = 0.74) in Parkinson's disease and 3.3 (1.4-7.6, P = 0.006) in parkinsonism compared with controls. Twenty-nine of the infected subjects died; 30-day fatality rate was 35.1%, without difference among the 3 groups. Six of 10 Parkinson's disease/parkinsonism patients had the infection during hospitalization or in a nursing home. CONCLUSIONS: Parkinson's disease per se probably is not a risk factor for COVID-19 hospitalization. Conversely, parkinsonism is an independent risk factor probably because of a more severe health status, entailing higher care dependence and placement in high-infection-risk accommodations. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

How health service delivery guides the allocation of major trauma patients in the intensive care units of the inclusive (hub and spoke) trauma system of the Emilia Romagna Region (Italy). A cross-sectional study.

OBJECTIVE: To evaluate cross-sectional patient distribution and standardised 30-day mortality in the intensive care units (ICU) of an inclusive hub and spoke trauma system. SETTING: ICUs of the Integrated System for Trauma Patient Care (SIAT) of Emilia-Romagna, an Italian region with a population of approximately 4.5 million. PARTICIPANTS: 5300 patients with an Injury Severity Score (ISS) >15 were admitted to the regional ICUs and recorded in the Regional Severe Trauma Registry between 2007 and 2012. Patients were classified by the Abbreviated Injury Score as follows: (1) traumatic brain injury (2) multiple injuriesand (3) extracranial lesions. The SIATs were divided into those with at least one neurosurgical level II trauma centre (TC) and those with a neurosurgical unit in the level I TC only. RESULTS: A higher proportion of patients (out of all SIAT patients) were admitted to the level I TC at the head of the SIAT with no additional neurosurgical facilities (1083/1472, 73.6%) compared with the level I TCs heading SIATs with neurosurgical level II TCs (1905/3815; 49.9%). A similar percentage of patients were admitted to level I TCs (1905/3815; 49.9%) and neurosurgical level II TCs (1702/3815, 44.6%) in the SIATs with neurosurgical level II TCs. Observed versus expected mortality (OE) was not statistically different among the three types of centre with a neurosurgical unit; however, the best mean OE values were observed in the level I TC in the SIAT with no neurosurgical unit. CONCLUSION: The Hub and Spoke concept was fully applied in the SIAT in which neurosurgical facilities were available in the level I TC only. The performance of this system suggests that competition among level I and level II TCs in the same Trauma System reduces performance in both. The density of neurosurgical centres must be considered by public health system governors before implementing trauma systems.

Diagnosis and treatment of rheumatoid arthritis in the Emilia Romagna region: a prospective population-based study.

OBJECTIVES: To perform a population-based study in rheumatoid arthritis (RA) patients, in order to evaluate the efficacy and safety of pharmacologic treatments. METHODS: 1087 patients with RA were enrolled; inclusion criteria were: newly diagnosed RA, already diagnosed RA with high disease activity (HDA) (DAS28≥4.2) starting biologic DMARDs (bDMARDs), already diagnosed RA with HDA continuing with conventional DMARDs (cDMARDs). The following data were collected: demographics, clinical and laboratory features, imaging and prescribed drugs. All parameters except immunology and imaging (performed yearly) were repeated at each follow-up evaluations (after 3, 6 and 12 months, and thereafter every 12 months). In order to evaluate clinical response, the EULAR response criteria were used as the gold standard. RESULTS: 414 (38.1%) newly diagnosed patients with RA, 477 (43.9%) RA patients who started bDMARDs and 196 (18.0%) RA patients who continued with cDMARDs were enrolled from April 2012 to March 2015 at 12 Rheumatology Centres in the Emilia Romagna Region. Statistical analyses showed a relative risk ratio (RRR) for moderate response of 1.65 in RA patients who started bDMARDs (p=0.16) and 2.49 for newly diagnosed RA (p=0.01). Sex, age and Health Assessment Questionnaire were not statistically significant. A RRR of 2.00 has been confirmed for RA patients who started bDMARDs (p<0.0005) for a good response as well as 2.20 for newly diagnosed RA (p<0.0005). An increase in adverse events among bDMARDs was found, but when looking at infections or neoplasia, no differences were highlighted between RA which started bDMARDs and RA who continued with cDMARDs. CONCLUSIONS: Our results are in line with already published papers from British and Swedish Registries: a greater likelihood to have a good response is demonstrated for not longstanding RA starting cDMARDs or RA with HDA when a bDMARD is started. Also a good safety profile is demonstrated.

A population based study on the night-time effect in trauma care.

BACKGROUND: The so-called off hour effect-that is, increased mortality for patients admitted outside normal working hours-has never been demonstrated in trauma care. However, most of the studies excluded transferred cases. Because these patients are a special challenge for trauma systems, we hypothesised that their processes of care could be more sensitive to the off hour effect. METHODS: The study design was retrospective, cohort and population based. We compared the mortality of all patients by daytime and night-time admittance to hospitals in an Italian region, with 4.5 million inhabitants, following a major injury in 2011. Logistic regression was used, adjusted for demographics and severity of injury (TMPM-ICD9), and stratified by transfer status. RESULTS: 1940 major trauma cases were included; 105 were acutely transferred. Night-time admission had a significant pejorative effect on mortality in the adjusted analysis (OR=1.49; 95% CI 1.05 to 2.11). This effect was most evident in transferred cases (OR=3.71; 95% CI 1.11 to 12.43). CONCLUSIONS: The night-time effect in trauma care was demonstrated for the first time and was maximal in transferred cases. This may explain why it was not found in previous studies where these patients were mostly excluded. Also, the use of population based data-whereby patients not accessing trauma centre care and presumably receiving poorer care were included-may have contributed to the findings.

Factors influencing surgical delay after hip fracture in hospitals of Emilia Romagna Region, Italy: a multilevel analysis.

Hip fracture is becoming a major public health concern, with associated mortality and morbidity particularly in the elderly. This study aims to investigate factors (i.e. patient factors and hospital variables) associated with increased risk for delaying surgery after hip fractures, and to assess whether and to what extent timing was associated with mortality risk. All patients aged 65 and over, resident in Emilia Romagna Region (Italy) and admitted to hospital for hip fracture (2009 - 2010) were selected. Data on surgical delay were adjusted using multilevel logistic regression model. A Cox proportional hazard model was fitted to a propensity score matched sample to assess mortality between delayed and early treated patients. Of the 10, 995 patients included (mean age: 83.3 years), 44.9% underwent surgery within 2 days. Significant risk factors for delayed surgery were: gender (OR: 1.16), comorbidity (OR: 1.29), anticoagulant (OR: 7.64) ,antiplatelet medication (OR: 2.43) , type of procedure (OR: 1.37) and day of admission (OR: Thu-Fri: 6.05; Sat-Sun: 1.17). Type of hospital and annual volume of hip fracture surgeries were not sufficient to explain hospital variability. A significant difference in mortality rate between early and delayed surgery emerged six months post surgery.

The counterintuitive effect of multiple injuries in severity scoring: a simple variable improves the predictive ability of NISS.

BACKGROUND: Injury scoring is important to formulate prognoses for trauma patients. Although scores based on empirical estimation allow for better prediction, those based on expert consensus, e.g. the New Injury Severity Score (NISS) are widely used. We describe how the addition of a variable quantifying the number of injuries improves the ability of NISS to predict mortality. METHODS: We analyzed 2488 injury cases included into the trauma registry of the Italian region Emilia-Romagna in 2006-2008 and assessed the ability of NISS alone, NISS plus number of injuries, and the maximum Abbreviated Injury Scale (AIS) to predict in-hospital mortality. Hierarchical logistic regression was used. We measured discrimination through the C statistics, and calibration through Hosmer-Lemeshow statistics, Akaike's information criterion (AIC) and calibration curves. RESULTS: The best discrimination and calibration resulted from the model with NISS plus number of injuries, followed by NISS alone and then by the maximum AIS (C statistics 0.775, 0.755, and 0.729, respectively; AIC 1602, 1635, and 1712, respectively). The predictive ability of all the models improved after inclusion of age, gender, mechanism of injury, and the motor component of Glasgow Coma Scale (C statistics 0.889, 0.898, and 0.901; AIC 1234, 1174, and 1167). The model with NISS plus number of injuries still showed the best performances, this time with borderline statistical significance. CONCLUSIONS: In NISS, the same weight is assigned to the three worst injuries, although the contribution of the second and third to the probability of death is smaller than that of the worst one. An improvement of the predictive ability of NISS can be obtained adjusting for the number of injuries.