Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project.

BackgroundDuring the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.MethodsData were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms' performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified.ResultsWe estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits.ConclusionImplementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.

The strange case of less C-sections: Hospital ownership, market concentration, and DRG-tariff regulation.

We evaluate the relationship between hospital ownership and responses to a policy providing large financial incentives for vaginal deliveries and financial disincentives for C-sections. We compare for-profit, nonprofit, and public hospitals operating in a public health care system organized according to the quasi-market model. We first theoretically show that hospital ownership matters insofar different hospitals are characterized by different ethical preferences. We also show that competition makes ownership less important. We then consider the case study of Lombardy in Italy. We exploit spatial variation in hospital ownership and in market concentration at the local level to evaluate the relationship between ownership and the probability of C-section. According to theory, empirical results strongly suggest that competitive pressures from alternative providers tend to homogenize behaviors. However, in local monopolies, in presence of a strong monetary incentive toward vaginal deliveries, we do observe less C-section from private for-profit hospitals than from public and private nonprofit hospitals, especially when C-sections are medically appropriate.

Patient satisfaction, patients leaving hospital against medical advice and mortality in Italian university hospitals: a cross-sectional analysis.

BACKGROUND: Healthcare systems are increasingly focusing on outcomes that are the endpoints of care: patient health status and patient satisfaction. The availability of patient satisfaction (PS) data has encouraged research on its relationship with other outcomes, such as mortality. In Italy, an inter-regional performance evaluation system (IRPES) provides 13 regional healthcare systems with a multidimensional assessment of appropriateness, efficiency, financial sustainability, effectiveness, and equity. For university hospitals, IRPES includes the percentage of patients leaving hospital against medical advice (PLHAMA) and mortality rates at the ward level. This paper investigates the relationship between PS and PLHAMA across and within regional healthcare systems in Italy. Secondly, PLHAMA is used as a PS proxy to investigate its relationship with mortality at the ward level in the IRPES university hospitals. METHODS: PLHAMA and mortality rates were gathered from administrative data, and PS scores from patient surveys. We explored the association between PS and PLHAMA through a correlation analysis, using data for the 13 IRPES regions. We tested this relationship also at the clinical directorate level in 28 hospitals in Tuscany (5482 interviewed patients in 100 clinical directorates). Secondly, we explored the association between PLHAMA and mortality at the ward level through correlation and regression analyses, using data of 405 wards of eight clinical specialties within 24 IRPES university hospitals. RESULTS: Lower PLHAMA rates were associated with a higher PS in both regional and clinical directorate levels. A positive association between PLHAMA and mortality was shown at the ward level for IRPES university hospitals, with different results for medical and surgical clinical specialties. CONCLUSIONS: PS is an important performance dimension that provides healthcare managers and professionals with useful insights for improving care quality and effectiveness. Based on the study results, the PLHAMA rate could be regularly measured to highlight patient dissatisfaction. Due to the association between PLHAMA and mortality, this study also provides evidence of the importance of the patient perspective in assessing the quality of healthcare services. This relationship proved to be significant for surgical clinical units, suggesting the need for further analysing outcomes considering their different determinants in medical and surgical care.

The redistributive effects of copayment in outpatient prescriptions: evidence from Lombardy.

BACKGROUND: In Italy, copayment has changed its nature and it can no longer be simply considered a system to curb inappropriate expenditure. It has become an important form of revenue for public health care provision, but it might also become a source of distortions in income and health benefits redistribution. METHODS: We use a rich administrative dataset gathering information on patients demand (whose records have been matched to income declared for tax purposes) to study the effects of an additional copayment (the so called "superticket" introduced by the Italian government in 2012) in Lombardy, the biggest Italian Region whose socio-economic dimension is comparable to that of many European countries (e.g., the Netherlands, Switzerland, etc.). RESULTS: Our analysis shows that at the aggregate level the non-uniform superticket schedule adopted in Lombardy is slightly pro-poor, but this result coexists with evidences pointing towards possible cases of restriction to access caused by the additional copayment. CONCLUSIONS: The introduction of the superticket and the ensuing increase in the out-of pocket payment for health care raises questions about the distribution of the burden among patients, and the sustainability of the extra revenue through time. This issue needs to be further investigated by combining health status data with the information in this dataset.

The effectiveness-efficiency trade-off in health care: The case of hospitals in Lombardy, Italy.

We study the presence and the magnitudes of trade-offs between health outcomes and hospitals' efficiency using a data set from Lombardy, Italy, for the period 2008-2011. Our goal is to analyze whether the pressures for cost containment may affect hospital performance in terms of population health status. Unlike previous work in this area, we analyze hospitals at the ward level so comparisons can be made across more homogeneous treatments. We focus on two different health outcomes: mortality and readmission rates. We find that there is a trade-off between mortality rates and efficiency, as more efficient hospitals have higher mortality rates. We also find, however, that more efficient hospitals have lower readmission rates. Moreover, we show that focusing the analysis at the ward level is essential, since there is evidence of higher mortality rates in general medicine and surgery, while in oncology mortality is lower in more efficient hospitals. Furthermore, we find that consideration of spatial processes is important since mortality rates are higher for hospitals subject to high degree of horizontal competition, but lower for those hospitals having strong competition but high efficiency. This implies that the interplay of efficient resource allocation and hospital competition is important for the sustainability and effectiveness of regional health care systems.

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.

Surfactant protein D (SP-D) as a biomarker of SARS-CoV-2 infection.

BACKGROUND: Surfactant protein-D (SP-D) is a lung-resident protein that has emerged as a potential biomarker for COVID-19. Previous investigations on acute respiratory distress syndrome patients demonstrated a significant increment of SP-D serum levels in pathological conditions. Since SP-D is not physiologically permeable to alveoli-capillary membrane and poorly expressed by other tissues, this enhancement is likely due to an impairment of the pulmonary barrier caused by prolonged inflammation. METHODS: A retrospective study on a relatively large cohort of patients of Hospital Pio XI of Desio was conducted to assess differences of the hematic SP-D concentrations among COVID-19 patients and healthy donors and if SP-D levels resulted a risk factor for disease severity and mortality. RESULTS: The first analysis, using an ANOVA-model, showed a significant difference in the mean of log SP-D levels between COVID-19 patients and healthy donors. Significant variations were also found between dead vs survived patients. Results confirm that SP-D concentrations were significantly higher for both hospitalized COVID-19 and dead patients, with threshold values of 150 and 250 ng/mL, respectively. Further analysis conducted with Logistic Mixed models, highlighted that higher SP-D levels at admission and increasing differences among follow-up and admission values resulted the strongest significant risk factors of mortality (model predictive accuracy, AUC = 0.844). CONCLUSIONS: The results indicate that SP-D can be a predictive marker of COVID-19 disease and its outcome. Considering its prognostic value in terms of mortality, the early detection of SP-D levels and its follow-up in hospitalized patients should be considered to direct the therapeutic intervention.

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.

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.

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.

Geographic variation in inpatient costs for Acute Myocardial Infarction care: Insights from Italy.

Geographic variations in healthcare expenditures have been widely reported within and between countries. Nevertheless, empirical evidence on the role of organizational factors and care systems in explaining these variations is still needed. This paper aims at assessing the regional differences in hospital spending for patients hospitalized for Acute Myocardial Infarction (AMI) in Tuscany and Lombardy regions (Italy), which rank high in terms of care quality and that have been, at least until 2016, characterized by quite different governance systems. Generalized linear models are performed to estimate index, 30-day and one-year hospitalization spending adjusted for baseline covariates. A two-part model is used to estimate 31-365 day expenditure. Adjusted hospital spending for AMI patients were significantly higher in Lombardy compared with Tuscany. In Lombardy, patients experienced higher re-hospitalizations in the 31-365 days and longer length of stays than in Tuscany. On the other hand, no significant regional differences in adjusted mortality rates at both acute and longer phases were found. Comparing two regional healthcare systems which mainly differ in both the reimbursement systems and the level of integration between hospital and community services provides insights into factors potentially contributing to regional variations in spending and, therefore, in areas for efficiency improvement.

Comparing health outcomes among hospitals: the experience of the Lombardy Region.

In recent years, governments and other stakeholders have increasingly used administrative data for measuring healthcare outcomes and building rankings of health care providers. However, the accuracy of such data sources has often been questioned. Starting in 2002, the Lombardy (Italy) regional administration began monitoring hospital care effectiveness on administrative databases using seven outcome measures related to mortality and readmissions. The present study describes the use of benchmarking results of risk-standardized mortality from Lombardy regional hospitals. The data usage is part of a general program of continuous improvement directed to health care service and organizational learning, rather than at penalizing or rewarding hospitals. In particular, hierarchical regression analyses - taking into account mortality variation across hospitals - were conducted separately for each of the most relevant clinical disciplines. Overall mortality was used as the outcome variable and the mix of the hospitals' output was taken into account by means of Diagnosis Related Group data, while also adjusting for both patient and hospital characteristics. Yearly adjusted mortality rates for each hospital were translated into a reporting tool that indicates to healthcare managers at a glance, in a user-friendly and non-threatening format, underachieving and over-performing hospitals. Even considering that benchmarking on risk-adjusted outcomes tend to elicit contrasting public opinions and diverging policymaking, we show that repeated outcome measurements and the development and dissemination of organizational best practices have promoted in Lombardy region implementation of outcome measures in healthcare management and stimulated interest and involvement of healthcare stakeholders.