Stratification of the risk of developing severe or lethal Covid-19 using a new score from a large Italian population: a population-based cohort study

Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy

BACKGROUND

Population-based Health Risk Assessment (HRA) tools are strategic for the implementation of integrated care. Various HRA algorithms have been developed in the last decades worldwide. Their full adoption being limited by technical, functional, and economical factors. This study aims to apply the Adjusted Morbidity Groups (AMG) algorithm in the context of an Italian Region, and evaluate its performance to support decision-making processes in healthcare programming.

METHODS

The pilot study used five Healthcare Administrative Databases (HADs) covering the period 2015-2021. An iterative semi-automated procedure was developed to extract, filter, check and merge the data. A technical manual was developed to describe the process, designed to be standardized, reproducible and transferable. AMG algorithm was applied and descriptive analysis performed. A dashboard structure was developed to exploit the results of the tool.

RESULTS

AMG produced information on the health status of Marche citizens, highlighting the presence of chronic conditions from age 45 years. Persons with high and very high level of complexity showed elevated mortality rates and an increased use of healthcare resources. A visualization dashboard was intended to provide to relevant stakeholders accessible, updated and ready-to-use aggregated information on the health status of citizens and additional insight on the use of the healthcare services and resources by specific groups of citizens.

CONCLUSION

The flexibility of the AMG, together with its ability to support policymakers and clinical sector, could favour its implementation in different scenarios across Europe. A clear strategy for the adoption of HRA tools and related key elements and lessons learnt for a successful transferability at the EU level were defined. HRA strategies should be considered a pillar of healthcare policies and programming to achieve person-centred care and promote the sustainability of the EU healthcare systems.

Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort Study

Background: Risk stratification models have been developed to identify patients that are at a higher risk of COVID-19 infection and severe illness. Objectives To develop and implement a scoring tool to identify COVID-19 patients that are at risk for severe illness during the Omicron wave. Methods: This is a retrospective cohort study that was conducted in Israel’s second-largest healthcare maintenance organization. All patients with a new episode of COVID-19 between 26 November 2021 and 18 January 2022 were included. A model was developed to predict severe illness (COVID-19-related hospitalization or death) based on one-third of the study population (the train group). The model was then applied to the remaining two-thirds of the study population (the test group). Risk score sensitivity, specificity, and positive predictive value rates, and receiver operating characteristics (ROC) were calculated to describe the performance of the model. Results: A total of 409,693 patients were diagnosed with COVID-19 over the two-month study period, of which 0.4% had severe illness. Factors that were associated with severe disease were age (age > 75, OR-70.4, 95% confidence interval [CI] 42.8–115.9), immunosuppression (OR-4.8, 95% CI 3.4–6.7), and pregnancy (5 months or more, OR-82.9, 95% CI 53–129.6). Factors that were associated with a reduced risk for severe disease were vaccination status (patients vaccinated in the previous six months OR-0.6, 95% CI 0.4–0.8) and a prior episode of COVID-19 (OR-0.3, 95% CI 0.2–0.5). According to the model, patients who were in the 10th percentile of the risk severity score were considered at an increased risk for severe disease. The model accuracy was 88.7%. Conclusions: This model has allowed us to prioritize patients requiring closer follow-up by their physicians and outreach services, as well as identify those that are most likely to benefit from anti-viral treatment during the fifth wave of infection in Israel, dominated by the Omicron variant.

Developing a comorbidity score in cancer patients using healthcare utilization databases during the COVID-19 pandemic: An experience from Italy

BACKGROUND

A strong relationship has been observed between comorbidities and the risk of severe/fatal COVID-19 manifestations, but no score is available to evaluate their association in cancer patients. To make up for this lacuna, we aimed to develop a comorbidity score for cancer patients, based on the Lombardy Region healthcare databases.

METHODS

We used hospital discharge records to identify patients with a new diagnosis of solid cancer between February and December 2019; 61 comorbidities were retrieved within 2 years before cancer diagnosis. This cohort was split into training and validation sets. In the training set, we used a LASSO-logistic model to identify comorbidities associated with the risk of developing a severe/fatal form of COVID-19 during the first pandemic wave (March-May 2020). We used a logistic model to estimate comorbidity score weights and then we divided the score into five classes (<=-1, 0, 1, 2-4, >=5). In the validation set, we assessed score performance by areas under the receiver operating characteristic curve (AUC) and calibration plots. We repeated the process on second pandemic wave (October-December 2020) data.

RESULTS

We identified 55,425 patients with an incident solid cancer. We selected 21 comorbidities as independent predictors. The first four score classes showed similar probability of experiencing the outcome (0.2% to 0.5%), while the last showed a probability equal to 5.8%. The score performed well in both the first and second pandemic waves: AUC 0.85 and 0.82, respectively. Our results were robust for major cancer sites too (i.e., colorectal, lung, female breast, and prostate).

CONCLUSIONS

We developed a high performance comorbidity score for cancer patients and COVID-19. Being based on administrative databases, this score will be useful for adjusting for comorbidity confounding in epidemiological studies on COVID-19 and cancer impact.

Role of telemedicine during COVID-19 pandemic in type 2 diabetes outpatients: The AMD annals initiative

AIMS

Telemedicine is advocated as a fundamental tool in modern clinical management. However, data on the effects of telemedicine vs face-to-face consultation on clinical outcomes in type 2 diabetes (T2DM) are still uncertain. This paper describes the use of telemedicine during the 2020 COVID-19 emergency and compares volume activity and quality indicators of diabetes care between face-to-face vs telemedicine counseling in the large cohort of T2DM patients from the AMD Annals Initiative.

METHODS

Demographic and clinical characteristics, including laboratory parameters, rate of the screening of long-term complications, current therapies and the Q-score, a validated score that measures the overall quality of care, were compared between 364,898 patients attending face-to-face consultation and 46,424 on telemedicine, during the COVID-19 pandemic.

RESULTS

Patients on telemedicine showed lower HbA1c levels (7.1 ± 1.2 % vs 7.3 ± 1.3 %, p < 0.0001), and they were less frequently treated with metformin, GLP1-RAs and SGLT2i and more frequently with DPP4i. The telemedicine group showed reduced monitoring of the various parameters considered as process indicators, especially, eye and foot examination. The proportion of patients with a good quality of care (Q score > 25) was higher among those receiving face-to-face consultation. Moreover, in the telemedicine group, all major clinical outcomes remained stable when further compared to those collected in the year 2019, when the same patients underwent a regular face-to-face consultation, suggesting that the care provided through telemedicine did not negatively affect the most important parameters.

CONCLUSIONS

During the COVID-19 pandemic, telemedicine provided an acceptable quality of diabetes care, comparable to that of patients attending face-to-face consultation, although a less frequent screening of complications seems to have occurred in subjects consulted by telemedicine.

Covid-19 vaccination programme effectiveness against SARS-CoV-2 related infections, hospital admissions and deaths in the Apulia region of Italy: a one-year retrospective cohort study

Studies reporting vaccine effectiveness against COVID-19 outcomes concentrate mainly on estimates of one single type of vaccine and variant, seldom considering waning effects. We aimed to estimate the effectiveness of the overall COVID-19 vaccination programme implemented in the Apulia region of Italy at preventing SARS-CoV-2 infections, COVID-19-related hospital admissions and deaths during alpha and delta variant dominant periods. We conducted a retrospective cohort study using electronic health records of persons 16 years and older resident in the Apulia region, assessing the effectiveness of the combined use of BNT162b2, mRNA-1273, ChAdOx1-S and Ad26.COV2.S vaccines against confirmed COVID-19 infections, hospitalisations and deaths, for fully and partially vaccinated persons as well as by time since vaccination and variants. Cox regression models yielding hazard ratios were used to calculate the overall vaccination programme effectiveness. From 1 January to 1 December 2021, we included 3,530,967 eligible persons in the cohort, of whom 2,770,299 were fully vaccinated and 158,313 were COVID-19 positive at the end of the study period. The effectiveness of the programme over the entire study period for fully vaccinated persons against COVID-19 infection, hospitalisation and death were 87.69% (CI95% 87.73-88.18), 94.08% (93.58-94.54) and 95.95% (CI95% 95.26-96.54), respectively. The effectiveness against COVID-19 infection of fully vaccinated subjects during the alpha and delta period was respectively 88.20% (CI95% 87.60-99.78) and 59.31% (CI95% 57.91-60.67), against hospitalisation 93.89% (CI95% 92.67-94.90) and 88.32% (CI95% 86.50-89.90) and against death 93.83% (CI95% 91.65-95.45) and 85.91 (CI95% 79.98-90.09). The waning effects of the programme regarding COVID-19 infection during the delta period were stronger than for alpha, with 75.85% (CI95% 74.38-77.24) effectiveness after 1-2 months and 8.35% (CI95% 3.45-13.01) after 5-6 months after full vaccination. The effectiveness against hospitalisation and death during the delta period waned rapidly and at 7-8-months after the full vaccination respectively decreased to 27.67% (CI95% 7.48-43.45) and 48.47 (CI95% 53.97-34.82). Our study suggests that the COVID-19 vaccination program in Apulia was strongly protective against COVID-19 infection, hospitalisation, and death due to alpha as well as delta variants, although its effectiveness is reduced over time.

Factors associated with severe or fatal clinical manifestations of SARS-CoV-2 infection after receiving the third dose of vaccine

BACKGROUND

Little is known about vulnerability to severe COVID-19 illness after vaccination completion with three doses of vaccine against COVID-19.

OBJECTIVES

To identify individual features associated with increased risk of severe clinical manifestation of SARS-CoV-2 infections after receiving the third dose of vaccine against COVID-19.

METHODS

We performed a nested case-control study based on 3,360,116 citizens from Lombardy, Italy, aged 12 years or older who received the third dose of vaccine against COVID-19 from 20 September through 31 December 2021. Individuals were followed from 14 days after vaccination completion until the occurrence of severe COVID-19 illness, death unrelated to COVID-19, emigration or 15 March 2022. For each case, controls were randomly selected to be 1:10 matched for the date of vaccination completion and municipality of residence. The association between candidate predictors and outcome was assessed through multivariable conditional logistic regression models.

RESULTS

During 12,538,330 person-months of follow-up, 5171 cases of severe illness occurred. As age increased, a trend towards increasing odds of severe illness was observed. Male gender was a significant risk factor. As the number of contacts with the Regional Health Service increased, a trend towards increasing odds of severe illness was observed. Having had a previous SARS-CoV-2 infection was a significant protective factor. Having received the Moderna vaccine significantly decreased the odds of severe illness. Significant higher odds were associated with 42 diseases/conditions. Odds ratios ranged from 1.23 (diseases of the musculoskeletal system) to 5.00 (autoimmune disease).

CONCLUSIONS

This study provides useful insights for establishing priority in fourth-dose vaccination programs.

Vulnerability Predictors of Post-Vaccine SARS-CoV-2 Infection and Disease-Empirical Evidence from a Large Population-Based Italian Platform

We aimed to identify individual features associated with increased risk of post-vaccine SARS-CoV-2 infection and severe COVID-19 illness. We performed a nested case–control study based on 5,350,295 citizens from Lombardy, Italy, aged ≥ 12 years who received a complete anti-COVID-19 vaccination from 17 January 2021 to 31 July 2021, and followed from 14 days after vaccine completion to 11 November 2021. Overall, 17,996 infections and 3023 severe illness cases occurred. For each case, controls were 1:1 (infection cases) or 1:10 (severe illness cases) matched for municipality of residence and date of vaccination completion. The association between selected predictors (sex, age, previous occurrence of SARS-CoV-2 infection, type of vaccine received, number of previous contacts with the Regional Health Service (RHS), and the presence of 59 diseases) and outcomes was assessed by using multivariable conditional logistic regression models. Sex, age, previous SARS-CoV-2 infection, type of vaccine and number of contacts with the RHS were associated with the risk of infection and severe illness. Moreover, higher odds of infection and severe illness were significantly associated with 14 and 34 diseases, respectively, among those investigated. These results can be helpful to clinicians and policy makers for prioritizing interventions.

Eight Good Reasons for Careful Monitoring and Evaluation of the Vaccine Campaign against COVID-19: Lessons Learned through the Lombardy Experience for Dealing with Next Challenges

Background: Using the knowledge gained during the first eleven months of the vaccine campaign in Lombardy, Italy, we provide an overview of the benefits of using reliable, complete, and rapidly available observational data to monitor the progress of the vaccine strategy. Methods: A population-based platform was implemented by linking four registries reporting individual data on: (i) date, type, and dose of vaccine dispensed; (ii) SARS-CoV-2 infections and hospital admissions and deaths due to COVID-19; (iii) inpatient diagnoses and outpatient services supplied by the Regional Health Services (RHS); and the (iv) health registry reporting and updating data on patient status. Background, methods, findings, and implications of eight COVID-19 relevant questions are reported. Results: Before starting the vaccine campaign, we identified high-risk individuals who need to be prioritized. During the vaccine campaign, we: (i) monitored the trend in the speed of the vaccine campaign progression and the number of prevented clinical outcomes; (ii) verified that available vaccines work in real-life, assessed their effectiveness-harm profile, and measured their reduced effectiveness against the delta variant. Finally, we studied the reduced effectiveness of the vaccine over time and identified risk factors of post-vaccine infection and severe illness. Conclusions: The correct use of rapidly available observational data of good quality and completeness generates reliable evidence to promptly inform patients and policymakers.