Analysis and clinical determinants of post-COVID-19 syndrome in the Lombardy region: evidence from a longitudinal cohort study.

OBJECTIVE: To define macro symptoms of long COVID and to identify predictive factors, with the aim of preventing the development of the long COVID syndrome. DESIGN: A single-centre longitudinal prospective cohort study conducted from May 2020 to October 2022. SETTING: The study was conducted at Luigi Sacco University Hospital in Milan (Italy). In May 2020, we activated the ARCOVID (Ambulatorio Rivalutazione COVID) outpatient service for the follow-up of long COVID. PARTICIPANTS: Hospitalised and non-hospitalised patients previously affected by COVID-19 were either referred by specialists or general practitioners or self-referred. INTERVENTION: During the first visit, a set of questions investigated the presence and the duration of 11 symptoms (palpitations, amnesia, headache, anxiety/panic, insomnia, loss of smell, loss of taste, dyspnoea, asthenia, myalgia and telogen effluvium). The follow-up has continued until the present time, by sending email questionnaires every 3 months to monitor symptoms and health-related quality of life. PRIMARY AND SECONDARY OUTCOME MEASURES: Measurement of synthetic scores (aggregation of symptoms based on occurrence and duration) that may reveal the presence of long COVID in different clinical macro symptoms. To this end, a mixed supervised and empirical strategy was adopted. Moreover, we aimed to identify predictive factors for post-COVID-19 macro symptoms. RESULTS: In the first and second waves of COVID-19, 575 and 793 patients (respectively) were enrolled. Three different post-COVID-19 macro symptoms (neurological, sensorial and physical) were identified. We found significant associations between post-COVID-19 symptoms and (1) the patients' comorbidities, and (2) the medications used during the COVID-19 acute phase. ACE inhibitors (OR=2.039, 95% CI: 1.095 to 3.892), inhaled steroids (OR=4.08, 95% CI: 1.17 to 19.19) and COVID therapies were associated with increased incidence of the neurological macro symptoms. Age (OR=1.02, 95% CI: 1.01 to 1.04), COVID-19 severity (OR=0.42, 95% CI: 0.21 to 0.82), number of comorbidities (OR=1.22, 95% CI: 1.01 to 1.5), metabolic (OR=2.52, 95% CI: 1.25 to 5.27), pulmonary (OR=1.87, 95% CI: 1.10 to 3.32) and autoimmune diseases (OR=4.57, 95% CI: 1.57 to 19.41) increased the risk of the physical macro symptoms. CONCLUSIONS: Being male was the unique protective factor in both waves. Other factors reflected different medical behaviours and the impact of comorbidities. Evidence of the effect of therapies adds valuable information that may drive future medical choices.

The impact of COVID-19 pandemic on AMI and stroke mortality in Lombardy: Evidence from the epicenter of the pandemic.

BACKGROUND: The first Covid-19 epidemic outbreak has enormously impacted the delivery of clinical healthcare and hospital management practices in most of the hospitals around the world. In this context, it is important to assess whether the clinical management of non-Covid patients has not been compromised. Among non-Covid cases, patients with Acute Myocardial Infarction (AMI) and stroke need non-deferrable emergency care and are the natural candidates to be studied. Preliminary evidence suggests that the time from onset of symptoms to emergency department (ED) presentation has significantly increased in Covid-19 times as well as the 30-day mortality and in-hospital mortality. METHODS: We check, in a causal inference framework, the causal effect of the hospital's stress generated by Covid-19 pandemic on in-hospital mortality rates (primary end-point of the study) of AMI and stroke over several time-windows of 15-days around the implementation date of the State of Emergency restrictions for COVID-19 (March, 9th 2020) using two quasi-experimental approaches, regression-discontinuity design (RDD) and difference-in-regression-discontinuity (DRD) designs. Data are drawn from Spedali Civili of Brescia, one of the most hit provinces in Italy by Covid-19 during March and May 2020. FINDINGS: Despite the potential adverse effects on expected mortality due to a longer time to hospitalization and staff extra-burden generated by the first wave of Covid-19, the AMI and stroke mortality rates are overall not statistically different during the first wave of Covid-19 than before the first peak. The obtained results provided by RDD models are robust also when we account for seasonality and unobserved factors with DRD models. INTERPRETATION: The non-statistically significant impact on mortality rates for AMI and stroke patients provides evidence of the hospital ability to manage -with the implementation of a dual track organization- the simultaneous delivery of high-quality cares to both Covid and non-Covid patients.

How have casemix, cost and hospital stay of inpatients in the last year of life changed over the past decade? Evidence from Italy.

Healthcare utilisation and expenditure are highly concentrated in hospital inpatient services, in particular in end-of-life care with the peak occurring in the very last year of life, regardless of patient age. Few scientific studies have investigated hospital costs and stays of patients at the end of life, and even fewer studies have analysed their evolution over time. In this paper, we exploit hospitalisation data for the Lombardy region of Italy with the aim of studying the evolution of hospital casemix, costs and stays of chronic patients, and compare the last year of life of two cohorts of patients who died in 2005 and 2014. Despite an overall three-year increase in the age at death, the results showed a significant decrease in hospital costs and use due to reduced interventions and length of hospital stays. However, this was not associated with an increase in quality of life/conditions (as indicated by clinical casemix as a proxy) for end-of-life patients; patients' casemix characteristics and clinical condition, as measured by the number of comorbidities, disease severity, prevalence of pulmonary disease and heart failure diagnosis, significantly worsened over the decade. This gives rise to important health policy concerns on how to identify effective policies and possible changes in healthcare system organisation to move from hospital-centred care to a community-centred approach whose value has been demonstrated during the COVID-19 pandemic.

A bivariate prediction approach for adapting the health care system response to the spread of COVID-19.

The spread of COVID-19 implied a large and fast increase of demand for intensive care services. To face this increase in demand, health care systems need to adapt their response by increasing hospital beds, intensive care unit (ICU) capacity and by (re-)deploying doctors and other personnel. This paper proposes a forecast approach based on the Vector Error Correction model for the daily counts of hospitalized patients with symptoms and of patients in ICU, using publicly available data on the current COVID-19 outbreak in Italy, Switzerland and Spain. The level of analysis is the local government managing the health care system response, which corresponds to regions for Italy. The one-week-ahead forecasts are validated with out-of-sample data over successive weeks; they are found to provide timely and robust prediction of ICU capacity needs in Lombardy, the most-affected Italian region, starting from the sample of the first 2 weeks of data. The same methodology is successfully validated on other Italian regions, Switzerland and Spain. This approach may be used in other countries/regions/provinces to help adapt the health care system response to COVID-19 (or other similar disease); for this purpose, the open-source software code to produce the forecasts is provided with the paper.

Monitoring the Italian Home Palliative Care Services.

BACKGROUND: In Italy, there currently is a lack of reliable and consistent data on home palliative care provided to people near death. OBJECTIVES: Monitoring the activities of the Italian Home Palliative Care Services, according to the 2014 national data collection program entitled "Observatory of Best Practices in Palliative Care" and providing process/outcome measures on a subsample (Best Practice Panel), on regulatory standards and on complete/reliable activity data. DESIGN: A data collection web portal using two voluntary internet-based questionnaires in order to retrospectively identify the main care activity data provided within the year 2013 by Home care units. In the Best Practice Panel and International best practices, eligibility and quality measures refer to the national standards of the NL 38/2010. Setting/Subject: Home Palliative Care Services (HPCSs) that provided care from January to December 2013. RESULTS: 118 Home care units were monitored, globally accounting for 40,955 assisted patients within the year 2013 (38,384 cancer patients); 56 (47.5% of 118) were admitted in the Best Practice Panel. Non-cancer (5%) and pediatric (0.4%) patients represented negligible percentages of frail care patients, and a majority of patients died at home (respectively nearly 75% and 80% of cancer and non-cancer patients). CONCLUSION: The study demonstrated the feasibility of the collection of certified data from Home care services through a web-based system. Only 80% of the facilities met the requirements provided by the Italian NL 38/2010. Moreover, the extension of the palliative care services provided to frail non-cancer and pediatric patients, affected by complex and advanced chronic conditions, is still inadequate in Italy.

The "ARIANNA" Project: An Observational Study on a Model of Early Identification of Patients with Palliative Care Needs through the Integration between Primary Care and Italian Home Palliative Care Units.

OBJECTIVE: The aim of this study was to illustrate the characteristics of patients with palliative care (PC) needs, early identified by general practitioners (GPs), and to analyze their care process in home PC services. BACKGROUND: Early identification and service integration are key components to providing quality palliative care (PC) services ensuring the best possible service for patients and their families. However, in Italy, PC is often provided only in the last phase of life and for oncological patients, with a fragmented service. METHODS: Multicenter prospective observational study, lasting in total 18 months, implemented in a sample of Italian Home Palliative Care Units (HPCUs), enrolling and monitoring patients with limited life expectancy, early identified by 94 GPs. The study began on March 1, 2014 and ended on August 31, 2015. RESULTS: Nine hundred thirty-seven patients, out of a total pool of 139,071, were identified by GPs as having a low life expectancy and PC needs. Of these, 556 (59.3%) were nononcological patients. The GPs sent 433 patients to the HPCUs for multidimensional assessment, and 328 (75.8%) were placed in the care of both settings (basic or specialist). For all patients included in the study, both oncological and nononcological patients, there was a high rate of death at home, around 70%. DISCUSSION: This study highlights how a model based on early identification, multidimensional evaluation, and integration of services can promote adequate PC, also for noncancer patients, with a population-based approach.

Quality assessment of palliative home care in Italy.

RATIONALE: The complexity of end-of-life care, represented by a large number of units caring for dying patients, of different types of organizations motivates the importance of measure the quality of provided care. Despite the law 38/2010 promulgated to remove the barriers and provide affordable access to palliative care, measurement, and monitoring of processes of home care providers in Italy has not been attempted. AIMS AND OBJECTIVES: Using data drawn by an institutional voluntary observatory established in Italy in 2013, collecting home palliative care units caring for people between January and December 2013, we assess the degree to which Italian home palliative care teams endorse a set of standards required by the 38/2010 law and best practices as emerged from the literature. METHODS: The evaluation strategy is based on Rasch analysis, allowing to objectively measuring both performances of facilities and quality indicators' difficulty on the same metric, using 14 quality indicators identified by the observatory's steering committee. RESULTS: Globally, 195 home care teams were registered in the observatory reporting globally 40 955 cured patients in 2013 representing 66% of the population of home palliative care units active in Italy in 2013. Rasch analysis identifies 5 indicators ("interview" with caregivers, continuous training provided to medical and nursing staff, provision of specialized multidisciplinary interventions, psychological support to the patient and family, and drug supply at home) easy to endorse by health care providers and 3 problematic indicators (presence of a formally established Local Network of Palliative care in the area of reference, provision of the care for most problematic patient requiring high intensity of the care, and the percentage of cancer patient dying at Home). CONCLUSIONS: The lack of Local Network of Palliative care, required by law 38/2010, is, at the present, the main barrier to its application. However, the adopted methodology suggests that a clear roadmap for health facilities to afford future quality and normative challenges.

Structural Equation Models in a Redundancy Analysis Framework With Covariates.

A recent method to specify and fit structural equation modeling in the Redundancy Analysis framework based on so-called Extended Redundancy Analysis (ERA) has been proposed in the literature. In this approach, the relationships between the observed exogenous variables and the observed endogenous variables are moderated by the presence of unobservable composites, estimated as linear combinations of exogenous variables. However, in the presence of direct effects linking exogenous and endogenous variables, or concomitant indicators, the composite scores are estimated by ignoring the presence of the specified direct effects. To fit structural equation models, we propose a new specification and estimation method, called Generalized Redundancy Analysis (GRA), allowing us to specify and fit a variety of relationships among composites, endogenous variables, and external covariates. The proposed methodology extends the ERA method, using a more suitable specification and estimation algorithm, by allowing for covariates that affect endogenous indicators indirectly through the composites and/or directly. To illustrate the advantages of GRA over ERA we propose a simulation study of small samples. Moreover, we propose an application aimed at estimating the impact of formal human capital on the initial earnings of graduates of an Italian university, utilizing a structural model consistent with well-established economic theory.

Benchmarking strategies for measuring the quality of healthcare: problems and prospects.

Over the last few years, increasing attention has been directed toward the problems inherent to measuring the quality of healthcare and implementing benchmarking strategies. Besides offering accreditation and certification processes, recent approaches measure the performance of healthcare institutions in order to evaluate their effectiveness, defined as the capacity to provide treatment that modifies and improves the patient's state of health. This paper, dealing with hospital effectiveness, focuses on research methods for effectiveness analyses within a strategy comparing different healthcare institutions. The paper, after having introduced readers to the principle debates on benchmarking strategies, which depend on the perspective and type of indicators used, focuses on the methodological problems related to performing consistent benchmarking analyses. Particularly, statistical methods suitable for controlling case-mix, analyzing aggregate data, rare events, and continuous outcomes measured with error are examined. Specific challenges of benchmarking strategies, such as the risk of risk adjustment (case-mix fallacy, underreporting, risk of comparing noncomparable hospitals), selection bias, and possible strategies for the development of consistent benchmarking analyses, are discussed. Finally, to demonstrate the feasibility of the illustrated benchmarking strategies, an application focused on determining regional benchmarks for patient satisfaction (using 2009 Lombardy Region Patient Satisfaction Questionnaire) is proposed.

Patient safety analysis linking claims and administrative data.

PURPOSE: The purpose of this paper is to provide international data on the occurrence (and rates) of clinical errors, identified by type and consequence in the Lombardy region, and to assess empirically the association between hospital accreditation-type measures and clinical error rates by merging hospital discharge records and medical malpractice claim data in the Lombardy region (Italy). DESIGN/METHODOLOGY/APPROACH: Data were drawn from the regional database collecting claims and demands for reimbursement declared by patients hospitalized in regional healthcare structures and regional archives collecting hospital discharge records. To model the variability of clinical errors rates, binomial negative regression models were applied. For improved interpretation of the results, a regression tree methodology was used. FINDINGS: The results demonstrated that the rate of readmission for the same major diagnostic category and the rate of discharges against medical advice significantly affect the incidence of errors causing patient death, whereas the rate of unscheduled surgical readmission in the operating room significantly affects the rate of surgical error. RESEARCH LIMITATIONS/IMPLICATIONS: The findings confirm that claims data is problematic in nature because of the limited number of claims generally emerging from administrative sources. The article proposes using proper regression models for count data, taking into account over-dispersion and excess zeroes and classification tree methods for a better interpretation of empirical evidence. PRACTICAL IMPLICATIONS: Health structures where quality outcomes have a significant impact on clinical error rates should be monitored in depth, investigating the medical charts of involved patients to identify quality problems and problematic areas. ORIGINALITY/VALUE: As a risk management strategy, the combined use of claims data and clinical administrative data is proposed to shed light on the more problematic, error-prone areas, allowing regional stakeholders to receive relevant, highly cost-effective and timely information and an in-depth understanding of the problematic areas in the assessment of risk.

Health of the nation outcome scales evaluation in a community setting population.

PURPOSE: To explore the internal structure of the health of the nation outcome scales (HoNOS-12), proposing a shorter one-dimensional version for routine use in community-oriented mental heath services. METHODS: A validation study involving four mental health departments, located in the province of Milan (Italy). Eligible patients were outpatients and residential inpatients rated on three occasions during the year 2009, with a range of mental illnesses and diagnoses. Methodologically, we use both exploratory factor analysis (EFA) with holdout validation and Rasch approaches and parallel analysis. RESULTS: EFA, Rasch analysis and parallel analysis demonstrate a large violation of unidimensionality. Both EFA (training sample) and Rasch analyses yield convergent results, generating the same unidimensional abbreviated version of the HoNOS-12, resulting in a six-item scale (HoNOS-6) which demonstrates unidimensionality, good item fit, a solid factor structure (strong loadings and communalities) and acceptable model fit, evaluated using confirmatory factor analysis on a validation sample. CONCLUSIONS: The HoNOS-12 does not measure a single, underlying construct of mental health status. Nevertheless, the instrument can be utilized in a reduced version (HoNOS-6), as a clinically acceptable outcome scale (measuring self-perceived clinical and social needs for community support, rather than global mental disorder) for routine use in a community setting population.

Patient safety analyses using Lombardy administrative archives.

PURPOSE: The purpose of this paper is to discuss strategies for benchmarking patient safety using Lombardy region administrative archives. Patient safety indicators and statistical methods are presented that allow risk adjustment. The analysis benchmarks regional health structures, focusing on two patient safety indicators: failure to rescue; and death in low mortality diagnostic related group. DESIGN/METHODOLOGY/APPROACH: Data were drawn from a research project promoted by the Italian Agency of Regional Health Services in 2002 to furnish statistical evidence regarding adverse events based on Agency for Healthcare Research and Quality indicators and methods. Hierarchical models for an equitable benchmark analyses are proposed. FINDINGS: Empirical analysis shows that hierarchical approaches, based on comparing health structures within homogenous specialties, disaggregates and moderates failure to rescue variabilities existing between hospitals, especially in oncology, intensive care and general medicine. RESEARCH LIMITATIONS/IMPLICATIONS: The paper proposes using hierarchical models for properly benchmarking health structures, resolving logistic regression drawbacks and limitations. PRACTICAL IMPLICATIONS: The paper strengthens the theory that accurate coding supported by software and administrative databases could provide a valuable and economical source for patient safety research. ORIGINALITY/VALUE: The paper analyses and suggests strategies for consistent benchmark analyses based on patient safety outcomes, applicable to several situations and different health structure typologies.