Sustained coronavirus disease (COVID-19) transmission is ongoing in Italy, with 7,375 reported cases and 366 deaths by 8 March 2020. We provide a modelbased evaluation of patient records from Lombardy, predicting the impact of an uncontrolled epidemic on the healthcare system. It has the potential to cause more than 250,039 (95% credible interval (CrI): 147,717-459,890) cases within 3 weeks, including 37,194 (95% CrI: 22,632) patients requiring intensive care. Aggressive containment strategies are required.On 20 February 2020, a case of coronavirus disease (COVID-19) was notified in Lombardy, Italy, uncovering ongoing transmission in at least two other regions (Emilia Romagna and Veneto) [1]. To rapidly assess risks of the current situation, we analysed the line list of reported cases in Lombardy to project the number of cases, should the epidemic be left uncontrolled. Epidemic projectionsWe projected the number of COVID-19 cases in 593 municipalities of Lombardy where at least one case of community transmission (i.e. excluding cases in healthcare workers or known nosocomial exposure) had been recorded by 8 March 2020. These represented 39.4% of all municipalities in the region and a total population of ca 6.9 million inhabitants (68.8% of the Lombardy population). The projections were based on a stochastic susceptible-infectious-removed (SIR) transmission dynamic model for each municipality and assuming that no control measures were in place. The model considered a population structured in 20 age groups (19 5-year age groups from 0 to 94 years and one age group for ≥ 95-year-olds) according to municipality-specific age distributions [2]. The Polymod contact matrix for Italy was incorporated to simulate the heterogeneity of contacts by age [3]. The model considered three consecutive infectious compartments to simulate a gamma distributed generation time of a mean of 6.6 days [1], longer than estimates obtained for Chinese provinces outside Hubei [4]. R 0 was sampled from the posterior distribution estimated for Lombardy: mean: 3.1, 95% credible interval (CrI): 2.9-3.2 [1]. We assumed asymptomatic and symptomatic individuals to be equally infectious as shown by preliminary analysis of virological data from the same region [1].We considered age-specific reporting and severity rates, which were estimated from the Lombardy line list. In particular, the fraction of reported infections at different ages r(a) was defined as where r m represents the overall fraction of reported infections in the population; i(a) is the proportion of expected infections in age a and p(a) is the proportion of cases reported in age group a. We computed i(a) as the average age distribution of cases at the end of 500 simulated epidemics in the model.Note that r(a) represents the probability that an infection occurring in age a is reported, while p(a) represents the contribution of reported cases of age a to the overall amount of reported cases. We estimated p(a) for seven age groups (0-19, 20-29, 30-39, 40-49, 50-59, 50-69 and ≥ 70 years). ...
Background: The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time; to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. Methods: This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February-June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February-June 2020, with non-missing hospital of admission and non-missing admission date.Results: The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56-80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1-28.0%); and steadily decreased from 34.6% (32.5-36.6%) in February to 7.6% (6.3-10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6-12.3) days, compared to 8.1 (7.8-8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5-22.8) days in February to 5.2 (4.7-5.8) days in June.Conclusions: The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2.
Background The aim of this study is to quantify the hospital burden of COVID-19 during the first wave and how it changed over calendar time; to interpret the results in light of the emergency measures introduced to manage the strain on secondary healthcare. Methods This is a cohort study of hospitalised confirmed cases of COVID-19 admitted from February–June 2020 and followed up till 17th July 2020, analysed using a mixture multi-state model. All hospital patients with confirmed COVID-19 disease in Regione Lombardia were involved, admitted from February–June 2020, with non-missing hospital of admission and non-missing admission date. Results The cohort consists of 40,550 patients hospitalised during the first wave. These patients had a median age of 69 (interquartile range 56–80) and were more likely to be men (60%) than women (40%). The hospital-fatality risk, averaged over all pathways through hospital, was 27.5% (95% CI 27.1–28.0%); and steadily decreased from 34.6% (32.5–36.6%) in February to 7.6% (6.3–10.6%) in June. Among surviving patients, median length of stay in hospital was 11.8 (11.6–12.3) days, compared to 8.1 (7.8–8.5) days in non-survivors. Averaged over final outcomes, median length of stay in hospital decreased from 21.4 (20.5–22.8) days in February to 5.2 (4.7–5.8) days in June. Conclusions The hospital burden, in terms of both risks of poor outcomes and lengths of stay in hospital, has been demonstrated to have decreased over the months of the first wave, perhaps reflecting improved treatment and management of COVID-19 cases, as well as reduced burden as the first wave waned. The quantified burden allows for planning of hospital beds needed for current and future waves of SARS-CoV-2 i.
BackgroundPay for Performance (P4P) programs, based on provision of financial incentives for service quality, have been widely adopted to enhance quality of care and to promote a more efficient use of health care resources whilst improving patient outcomes. In Italy, as in other countries, the growing concern over the quality of health services provided and the scarcity of resources would make P4P programs a useful means of improving their performance. The aim of this paper is to evaluate whether it is possible to implement P4P programs in the Lombardy Region, in Italy, based on the existing data set.MethodsThirteen quality measures were identified regarding four clinical conditions (acute myocardial infarction (AMI), heart failure (HF), ischemic stroke and hip and knee replacement) on the basis of an international literature review. Data was collected using the database of three institutions, which included hospital discharge records (Scheda di Dimissione ospedaliera-SDO-) and letters of discharge. The study population was identified using both the Principal ICD-9-CM diagnosis codes and the discharge date. A Statistical Analysis System (SAS) program was used for the text analysis.ResultsIt was possible to calculate almost all the parameters pertaining to the three hospitals as all the data required was available with the exception of inpatient mortality in two hospitals and smoking cessation advice/counseling in one hospital.ConclusionsOn the ground of this analysis, we believe that it is possible to implement a P4P program in the Lombardy Region. However, for this program to be initiated, all necessary data must be available in electronic format and uniformly collected. Moreover, several other factors must be assessed: which clinical conditions should be included, the threshold for each quality parameter, the amount of financial incentives offered and how they will be provided.
Objective: The aim of the present study was to analyze the IVF success rates and the economic cost per delivery in all the public funded IVF Units in Lombardy in the 2017–2018 period and to assess any significant difference in ART outcomes among the enrolled centers.Methods: Analysis of costs for the 2017 and 2018 fresh transfer delivery rate (DR) and Cumulative delivery rate (CDR) considering both fresh and frozen cycles were extracted from the ART Italian Registry on oocytes retrievals, fresh and frozen embryos and oocytes embryo transfer performed in 22 Lombardy IVF Units.Results: In 2017, 29,718 procedures were performed, resulting in 4,543 pregnancies and 3,253 deliveries. In 2018, there were 29,708 procedures, 4,665 pregnancies and 3,348 deliveries. Pregnancies lost to follow up were 5.0% with a (range of 0–67.68%) in 2017 and 3.4% (range of 0–45.1%) in 2018. The cost reimbursement for the cycles were €2,232 ($2,611) for oocyte retrieval and €2,194 ($2,567) for embryo transfer, excluding ovarian stimulation therapy and luteal phase support. 19.33 (5.80). The DR was 13.23 ± 5.69% (range 2.86–29.11%) in 2017 and 19.33 ± 5.80% in 2018 (range 11.82–34.98 %) and the CDR was 19.86 ± 9.38% (range 4.43–37.88%) in 2017 and 21.32 ± 8.84% (range 4.24–37.11%). The mean multiple pregnancy delivery rate (MDR) was 11.08 ± 5.55% (range 0.00–22.73%) in 2017 and 10.41 ± 4.99% (range 1.33–22.22%) in 2018. The mean CDR cost in euros was 26,227 ± 14,737 in 2017 and 25,018 ± 16,039 in 2018. The mean CDR cost among centers was 12,480 to 76,725 in 2017 and 12,973 to 86,203 in 2018.Conclusions: Our findings show impressive differences in the DR and CDR among centers and the importance of cryopreservation in patients' safety and economic cost reduction suggesting the formulation of specific KPI's (Key performance indexes) and minimal performance indexes (PI) as a basis for the allocation of public or insurance resources. In particular, the reduction of multiple pregnancy rates costs, may lead to a more widespread use of ART even in lower resources countries.
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