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BackgroundThe pandemic may undermine the equity of access to and utilisation of health services for conditions other than COVID-19. The objective of the study is to evaluate the indirect impact of COVID-19 and lockdown measures on sociodemographic inequalities in healthcare utilisation in seven Italian areas.MethodsIn this multicentre retrospective study, we evaluated whether COVID-19 modified the association between educational level or deprivation and indicators of hospital utilisation and quality of care. We also assessed variations in gradients by sex and age class. We estimated age-standardised rates and prevalence and their relative per cent changes comparing pandemic (2020) and pre-pandemic (2018–2019) periods, and the Relative Index of Inequalities (RIIs) fitting multivariable Poisson models with an interaction between socioeconomic position and period.ResultsCompared with 2018–2019, hospital utilisation and, to a lesser extent, timeliness of procedures indicators fell during the first months of 2020. Larger declines were registered among women, the elderly and the low educated resulting in a shrinkage (or widening if RII ConclusionsThe socially patterned reduction of hospital utilisation may lead to a potential exacerbation of health inequalities among groups who were already vulnerable before the pandemic. The healthcare service can contribute to contrast health disparities worsened by COVID-19 through more efficient communication and locally appropriate interventions.

to evaluate and validate the adoption of an algorithm for the identification of cases of congenital anomalies (CAs) to improve the performance of the Congenital Malformations Registry of Sicily Region (Southern Italy).an algorithm was used to identify congenital anomalies on a sample of hospital discharge records (SDO) with ICD-9-CM code between 740-759 on any of the diagnoses within the first year of life, together with a sample of healthy births equal to 5% of total births for the same period. The identified cases were evaluated through the clinical record analysis.the analysed sample was composed of 4,271 cases identified between June 2013 and December 2014 along with 3,993 SDO without any code of MC (5% of the total volume of births in the same period).positive predictive value (VPP) and negative predictive value (VPN) were computed by means of the comparison between the algorithm outcomes and the clinical record verification.4,271 potentially malformed records involving 3,381 subjects born in the Sicilian territory have been identified. Among the hospital discharge records that it was possible to verify, the application of the algorithm led to the exclusion of 924 cases: of these, 62 proved to be false negatives (VPN: 93.3). The valid cases were 1,179, while the cases to be evaluated 617: the comparison between algorithm and clinical record analysis led to a VPP of 91.7 and 72.1, respectively, for valid and to be evaluated.the tested algorithm proved to be a useful tool for identifying SDO potentially related to congenital anomalies. In the overall sample, the algorithm provided an outcome consistent with the clinical record assessment in 87.4% (2,379) of cases.

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.

Objectives: to evaluate and validate the adoption of an algorithm for the identification of cases of congenital anomalies (CAs) to improve the performance of the Congenital Malformations Registry of Sicily Region (Southern Italy). Design: an algorithm was used to identify congenital anomalies on a sample of hospital discharge records (SDO) with ICD-9-CM code between 740-759 on any of the diagnoses within the first year of life, together with a sample of healthy births equal to 5% of total births for the same period. The identified cases were evaluated through the clinical record analysis. Setting and participants: the analysed sample was composed of 4,271 cases identified between June 2013 and December 2014 along with 3,993 SDO without any code of MC (5% of the total volume of births in the same period). Main outcome measures: positive predictive value (VPP) and negative predictive value (VPN) were computed by means of the comparison between the algorithm outcomes and the clinical record verification. Results: 4,271 potentially malformed records involving 3,381 subjects born in the Sicilian territory have been identified. Among the hospital discharge records that it was possible to verify, the application of the algorithm led to the exclusion of 924 cases: of these, 62 proved to be false negatives (VPN: 93.3). The valid cases were 1,179, while the cases to be evaluated 617: the comparison between algorithm and clinical record analysis led to a VPP of 91.7 and 72.1, respectively, for valid and to be evaluated. Conclusions: the tested algorithm proved to be a useful tool for identifying SDO potentially related to congenital anomalies. In the overall sample, the algorithm provided an outcome consistent with the clinical record assessment in 87.4% (2,379) of cases.

Prevalence of Parkinson’s disease (PD) increases with the advanced ages, representing a relevant health burden. Accurate prevalence estimates are of fundamental need in order to adjust the supply of health services for these patients. The availability of administrative health data from the National Health System provides a useful resource to assess the burden of diseases. Our aim was to evaluate the prevalence of PD through the use of administrative data in the Sicily region. We have identified all the subjects affected by PD in Sicily in 2017 by gathering data from three regional health administrative databases: the hospital discharge records, the medical exemption databases, and the pharmacological prescription database. Prevalence rates and 95% confidence intervals (CI) have been calculated across 5-year age classes. PD patients identified through database searching were 24,674, giving a prevalence of 488/100,000 (95%CI 481.9–494.1) inhabitants. Prevalence was higher among men (514.5/100,000; 95%CI 505.6–523.6) and reached a peak in the 85–89 age class (3203.8/100,000; 95%CI 3095.2–3315.1). Our prevalence estimates of PD were higher when compared to previous epidemiological surveys conducted in Sicily. These findings are, however, comparable to other studies conducted in Italy that identified cases through administrative databases. Using health databases is a feasible strategy to assess the burden of PD.

The COVID-19 pandemic has a non-negligible indirect impact on hospital care pathways, which is important to estimate. To this aim, we set up the Mimico-19 network of seven Italian regions (62% of the Italian population) representing different socio-demographic areas of the country with also a different burden of the epidemic. We retrospectively analysed regional hospital discharges data, computing twelve indicators of volumes and performance in three clinical areas: cardiology, oncology and orthopaedics, including time-dependent pathways and elective surgery. Weekly indicators for the period January-July 2020 were compared with the average of the corresponding indicators in 2018 and 2019; comparisons were performed within 3 sub-periods: pre-lockdown, lockdown and post-lockdown. The weekly trend of hospitalizations for ST-segment elevation myocardial infarction (STEMI) showed a 40% reduction, but the proportion of STEMI patients with a primary PTCA did not significantly change from previous years. Volumes of malignant neoplasms surgeries differed substantially by site, with a limited reduction for lung cancer (