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Background During the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections. Aim To develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas. Methods Data 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. Results We 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. Conclusion Implementing 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.

BACKGROUND: Although trastuzumab (T) represents the standard of care for the adjuvant treatment of HER2-positive early-stage breast cancer, contrasting results are available about the cardiac toxicity associated to its use. We conducted a multiregional population-based cohort investigation aimed to assess both the short- and long-term cardiovascular (CV) outcomes in women with early breast cancer treated with T-based or standard adjuvant chemotherapy (CT). MATERIALS AND METHODS: We used health care use databases of six Italian regions, overall accounting for 42% of the Italian population. The study cohort was made by all women surgically treated for breast cancer who started a first-line adjuvant T-based or CT treatment. Patients treated with T were 1:2 matched to those treated with CT based on date of treatment start, age, and presence of CV risk factors. Short- and long-term CV outcomes (heart failure and cardiomyopathy) were measured, respectively, after 1 year and at the end of follow-up. RESULTS: Among 28,599 women who met the inclusion criteria, 6,208 T users were matched to 12,416 CT users. After a mean follow-up of 5.88 years, short- and long-term cumulative CV risk were 0.8% and 2.6% in patients treated with T and 0.2% and 2.8% in those treated with CT, respectively. Adjusted hazard ratios were 4.6 (95% confidence interval [CI], 2.6-8.0) for short-term and 1.2 (95% CI, 0.9-1.6) for long-term CV risk. DISCUSSION: In our large real-world investigation, T-associated cardiotoxicity was limited to the treatment period. The addition of T to adjuvant CT did not result in long-term worsening of CV events. IMPLICATIONS FOR PRACTICE: Adjuvant trastuzumab-based chemotherapy represents the backbone therapy in patients with HER2-positive early breast cancer. Although well tolerated, cardiovascular events can manifest during or after therapy because of treatment-related toxicities. In this wide multicenter and unselected cohort, long-term symptomatic cardiotoxicity was low and limited to the treatment period. The findings suggest that developing tools that would be adequately able to predict cardiac toxicity at an early stage remains an important area in which additional research efforts are needed.

Background: Preterm birth is a major worldwide public health concern, being the leading cause of infant mortality. Understanding of risk factors remains limited, and early identification of women at high risk of preterm birth is an open challenge. Objective: The aim of the study was to develop and validate a novel pre-pregnancy score for preterm delivery in nulliparous women using information from Italian healthcare utilization databases. Study Design: Twenty-six variables independently able to predict preterm delivery were selected, using a LASSO logistic regression, from a large number of features collected in the 4 years prior to conception, related to clinical history and socio-demographic characteristics of 126,839 nulliparous women from Lombardy region who gave birth between 2012 and 2017. A weight proportional to the coefficient estimated by the model was assigned to each of the selected variables, which contributed to the Preterm Birth Score. Discrimination and calibration of the Preterm Birth Score were assessed using an internal validation set (i.e., other 54,359 deliveries from Lombardy) and two external validation sets (i.e., 14,703 and 62,131 deliveries from Marche and Sicily, respectively). Results: The occurrence of preterm delivery increased with increasing the Preterm Birth Score value in all regions in the study. Almost ideal calibration plots were obtained for the internal validation set and Marche, while expected and observed probabilities differed slightly in Sicily for high Preterm Birth Score values. The area under the receiver operating characteristic curve was 60%, 61% and 56% for the internal validation set, Marche and Sicily, respectively. Conclusions: Despite the limited discriminatory power, the Preterm Birth Score is able to stratify women according to their risk of preterm birth, allowing the early identification of mothers who are more likely to have a preterm delivery.