Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.

Introduction: Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system. Methods: The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence. Results: Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche. Conclusion: Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Early Detection of SARS-CoV-2 Epidemic Waves: Lessons from the Syndromic Surveillance in Lombardy, Italy.

We evaluated the performance of the exponentially weighted moving average (EWMA) model for comparing two families of predictors (i.e., structured and unstructured data from visits to the emergency department (ED)) for the early detection of SARS-CoV-2 epidemic waves. The study included data from 1,282,100 ED visits between 1 January 2011 and 9 December 2021 to a local health unit in Lombardy, Italy. A regression model with an autoregressive integrated moving average (ARIMA) error term was fitted. EWMA residual charts were then plotted to detect outliers in the frequency of the daily ED visits made due to the presence of a respiratory syndrome (based on coded diagnoses) or respiratory symptoms (based on free text data). Alarm signals were compared with the number of confirmed SARS-CoV-2 infections. Overall, 150,300 ED visits were encoded as relating to respiratory syndromes and 87,696 to respiratory symptoms. Four strong alarm signals were detected in March and November 2020 and 2021, coinciding with the onset of the pandemic waves. Alarm signals generated for the respiratory symptoms preceded the occurrence of the first and last pandemic waves. We concluded that the EWMA model is a promising tool for predicting pandemic wave onset.

Trends in heart failure hospitalizations, patient characteristics, in-hospital and 1-year mortality: A population study, from 2000 to 2012 in Lombardy.

BACKGROUND: This study was undertaken to evaluate trends in heat failure hospitalizations (HFHs) and 1-year mortality of HFH in Lombardy, the largest Italian region, from 2000 to 2012. METHODS: Hospital discharge forms with HF-related ICD-9 CM codes collected from 2000 to 2012 by the regional healthcare service (n=699797 in 370538 adult patients), were analyzed with respect to in-hospital and 1-year mortality; Group (G) 1 included most acute HF episodes with primary cardiac diagnosis (70%); G2 included cardiomyopathies without acute HF codes (17%); and G3 included non-cardiac conditions with HF as secondary diagnosis (13%). Patients experiencing their first HFH since 2005 were analyzed as incident cases (n=216782). RESULTS: Annual HFHs number (mean 53830) and in-hospital mortality (9.4%) did not change over the years, the latter being associated with increasing age (p<0.0001) and diagnosis Group (G1 9.1%, G2 5.6%, G3 15.9%, p<0.0001). Incidence of new cases decreased over the years (3.62 [CI 3.58-3.67] in 2005 to 3.13 [CI 3.09-3.17] in 2012, per 1000 adult inhabitants/year, p<0.0001), with an increasing proportion of patients aged ≥85y (22.3% to 31.4%, p<0.0001). Mortality lowered over time in <75y incident cases, both in-hospital (5.15% to 4.36%, p<0.0001) and at 1-year (14.8% to 12.9%, p=0.0006). CONCLUSIONS: The overall burden and mortality of HFH appear stable for more than a decade. However, from 2005 to 2012, there was a reduction of new, incident cases, with increasing age at first hospitalization. Meanwhile, both in-hospital and 1-year mortality decreased in patients aged <75y, possibly due to improved prevention and treatment.

Methodological issues on the use of administrative data in healthcare research: the case of heart failure hospitalizations in Lombardy region, 2000 to 2012.

BACKGROUND: Administrative data are increasingly used in healthcare research. However, in order to avoid biases, their use requires careful study planning. This paper describes the methodological principles and criteria used in a study on epidemiology, outcomes and process of care of patients hospitalized for heart failure (HF) in the largest Italian Region, from 2000 to 2012. METHODS: Data were extracted from the administrative data warehouse of the healthcare system of Lombardy, Italy. Hospital discharge forms with HF-related diagnosis codes were the basis for identifying HF hospitalizations as clinical events, or episodes. In patients experiencing at least one HF event, hospitalizations for any cause, outpatient services utilization, and drug prescriptions were also analyzed. RESULTS: Seven hundred one thousand, seven hundred one heart failure events involving 371,766 patients were recorded from 2000 to 2012. Once all the healthcare services provided to these patients after the first HF event had been joined together, the study database totalled about 91 million records. Principles, criteria and tips utilized in order to minimize errors and characterize some relevant subgroups are described. CONCLUSIONS: The methodology of this study could represent the basis for future research and could be applied in similar studies concerning epidemiology, trend analysis, and healthcare resources utilization.