Treatment of lung cancer depends on the stage of the tumor and the histological type. In recent years, the histological confirmation of lung non-small-cell lung cancer has become crucial since the availability of selective target therapeutic approaches. The aim of the study was to develop a validated procedure to estimate the incidence and prevalence of non-small-cell and small-cell lung cancer from healthcare administrative data. A latent class model for categorical variables was applied. The following observed variables were included in the analysis: ICD-9-CM codes in the Hospital Discharge Registry, ATC codes of medications dispensed present in the Drugs Prescriptions Registry, and the procedure codes in the Outpatient Registry. The proportion of non-small-cell lung cancer diagnoses was estimated to be 85% of the total number of lung cancer on the cohort of incident cases and 89% on the cohort of prevalent cases. External validation on a cohort of 107 patients with a lung cancer diagnosis and histological confirmation showed a sensitivity of 95.6% (95%CI: 89–98.8%) and specificity of 94.1% (95%CI: 71.3–99.9%). The procedure is an easy-to-use tool to design subpopulation-based studies on lung cancer and to better plan resource allocation, which is important since the introduction of new targeted therapies in non-small-cell lung carcinoma.

Testing for the SARS-CoV-2 infection is critical for tracking the spread of the virus and controlling the transmission dynamics. In the early phase of the pandemic in Italy, the decentralized healthcare system allowed regions to adopt different testing strategies. The objective of this paper is to assess the impact of the extensive testing of symptomatic individuals and their contacts on the number of hospitalizations against a more stringent testing strategy limited to suspected cases with severe respiratory illness and an epidemiological link to a COVID-19 case. A Poisson regression modelling approach was adopted. In the first model developed, the cumulative daily number of positive cases and a temporal trend were considered as explanatory variables. In the second, the cumulative daily number of swabs was further added. The explanatory variable, given by the number of swabs over time, explained most of the observed differences in the number of hospitalizations between the two strategies. The percentage of the expected error dropped from 70% of the first, simpler model to 15%. Increasing testing to detect and isolate infected individuals in the early phase of an outbreak improves the capability to reduce the spread of serious infections, lessening the burden of hospitals.

Background: Poly(ADP-ribose) polymerase inhibitor (PARPi) treatment in ovarian cancer patients after first-line chemotherapy and following the response to platinum-based chemotherapy at relapse is associated with survival benefits. Maintenance therapies can be administered over extended periods, making treatment tolerability assessment essential in optimizing patient outcomes. This cohort study aimed to evaluate the agreement between physician and patient reporting of PARP inhibitor-related toxicities and the rate of underestimation of each symptom considered. Methods: Patients treated with PARPis in the first-line or recurrent setting were included. A specific Patient-Reported Outcomes Common Terminology Criteria for Adverse Events (PRO-CTCAE) questionnaire was generated and administered to the cohort. For each toxicity, agreement between patients and physicians was assessed using Cohen’s kappa and Gwet’s AC1; in addition, the rate of toxicity under-reporting by physicians was calculated. Results: Seventy-seven ovarian cancer patients were included; 39 (50.6%) received PARPis in the first-line setting, while 38 (49.4%) were treated for recurrence. Cohen’s kappa values for agreement between patients and physicians across 12 reported toxicities ranged from 0 to 0.15, indicating poor agreement (κ < 0.20) for all assessed toxicities, with the lowest levels of agreement for decreased appetite (κ = 0), rash (κ = 0.02), headache (κ = 0.00), arthralgia (κ = 0.03), insomnia (κ = 0.03), and fatigue (κ = 0.04). When agreement was assessed using Gwet’s AC1, agreement remained poor to moderate for the majority of the symptoms evaluated. Physician under-reporting rates were higher for nausea (51.9%), rash (57.1%), headache (49.3%), arthralgia (70.2%), insomnia (48.1%), and fatigue (67.5%). Conclusions: Our results underscore the importance of systematically integrating patient-reported outcomes into clinical practice, including in maintenance settings, to ensure an accurate assessment of treatment-related toxicities.