Drug-utilisation Profiles and COVID-19: Retrospective Cohort Study in Italy

Orlando, Valentina; Coscioni, Enrico; Guarino, Ilaria; Mucherino, Sara; Perrella, Alessandro; Trama, Ugo; Limongelli, Giuseppe; Menditto, Enrica

doi:10.21203/rs.3.rs-31829/v1

Cited by 4 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This allowed to Campania Region of designing, building and routinely managing the so-called Campania Region Database (CaReDB) which formed the data source for the current study. Completeness and data validity of CaReDB being elsewhere reported [ 13 – 21 ]. Selected characteristics of CaReDB are described in S1 Table .…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: A population-based cohort study from Italy

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background The novel coronavirus (SARS-CoV-2) pandemic spread rapidly worldwide increasing exponentially in Italy. To date, there is lack of studies describing clinical characteristics of the people at high risk of infection. Hence, we aimed (i) to identify clinical predictors of SARS-CoV-2 infection risk, (ii) to develop and validate a score predicting SARS-CoV-2 infection risk, and (iii) to compare it with unspecific scores. Methods Retrospective case-control study using administrative health-related database was carried out in Southern Italy (Campania region) among beneficiaries of Regional Health Service aged over than 30 years. For each person with SARS-CoV-2 confirmed infection (case), up to five controls were randomly matched for gender, age and municipality of residence. Odds ratios and 90% confidence intervals for associations between candidate predictors and risk of infection were estimated by means of conditional logistic regression. SARS-CoV-2 Infection Score (SIS) was developed by generating a total aggregate score obtained from assignment of a weight at each selected covariate using coefficients estimated from the model. Finally, the score was categorized by assigning increasing values from 1 to 4. Discriminant power was used to compare SIS performance with that of other comorbidity scores. Results Subjects suffering from diabetes, anaemias, Parkinson’s disease, mental disorders, cardiovascular and inflammatory bowel and kidney diseases showed increased risk of SARS-CoV-2 infection. Similar estimates were recorded for men and women and younger and older than 65 years. Fifteen conditions significantly contributed to the SIS. As SIS value increases, risk progressively increases, being odds of SARS-CoV-2 infection among people with the highest SIS value (SIS = 4) 1.74 times higher than those unaffected by any SIS contributing conditions (SIS = 1). Conclusion Conditions and diseases making people more vulnerable to SARS-CoV-2 infection were identified by the current study. Our results support decision-makers in identifying high-risk people and adopting of preventive measures to minimize the spread of further epidemic waves.

show abstract

Section: Methodsmentioning

confidence: 99%

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: A population-based cohort study from Italy

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the purpose of this analysis, data from 1 January 2017 through 31 December 2018 were used. CaReDB is complete and includes validated data used in previous drug utilization studies ( Iolascon et al, 2013 ; Casula et al, 2014 ; Orlando et al, 2016 ; Guerriero et al, 2017 ; Russo et al, 2018 ; Orlando et al, 2020 ). The characteristics of CaReDB are described in Supplementary Table S1 .…”

Section: Methodsmentioning

confidence: 99%

Comorbidity in an Older Population with Type-2 Diabetes Mellitus: Identification of the Characteristics and Healthcare Utilization of High-Cost Patients

et al. 2020

Self Cite

View full text Add to dashboard Cite

Objectives: Little is known about the specific comorbidities contributing to higher costs in patients with type-2 diabetes mellitus (T2DM), particularly in older cases. We aimed to evaluate the prevalence, type, and cost of comorbidities occurring in older T2DM patients versus older non-T2DM patients, and the factors associated with high cost (HC) T2DM patients.Methods: Retrospective cohort study using information from the Campania Region healthcare database. People aged ≥65 years who received ≥2 prescriptions for antidiabetic drugs were identified as “T2DM patients.” Comorbidities among T2DM and non-T2DM groups were assessed through the RxRiskV Index (modified version). T2DM individuals were classified according to the total cost distribution as HC or “non-high cost.” Two sub-cohorts of HC T2DM patients were assessed: above 90th and 80th percentile of the total cost. Age- and sex-adjusted logistic regression models were created.Results: Among the T2DM cohort, concordant and discordant comorbidities occurred significantly more frequently than in the non-T2DM cohort. Total mean annual cost per T2DM patient due to comorbidities was €7,627 versus €4,401 per non-T2DM patient. Among T2DM patients identified as being above 90th and 80th percentiles of cost distribution, the total annual costs were >€19,577 and >€2,563, respectively. The hospitalization cost was higher for T2DM cases. Strongest predictors of being a HC T2DM patient were having ≥5 comorbidities and renal impairment.Conclusion: HC patients accrued >80% of the total comorbidities cost in older T2DM patients. Integrated care models, with holistic and patient-tailored foci, could achieve more effective T2DM care.

show abstract

“…The Campania Region Database (CaReDB) includes information on patient demographics, the electronic records of outpatient pharmacy dispensing and hospital discharges for ~6 million residents of a well-defined population in Italy (~10% of the population of Italy). CaReDB is complete and includes validated data in previous drug utilization studies [9][10][11][12][13][14][15][16][17]. The characteristics of CaReDB are described in S1 Table. From the beginning of the Covid-19 epidemic, a surveillance system was implemented to collect all cases identified by reverse transcription-polymerase chain reaction (RT-PCR) testing for SARS-CoV-2.…”

Section: Target Population and Data Sourcementioning

confidence: 99%

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy

Orlando

Rea

Savaré

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

BackgroundThe novel coronavirus (SARS-CoV-2) pandemic spread rapidly worldwide increasing exponentially in Italy. To date, there is lack of studies describing clinical characteristics of the population most at risk of infection. Hence, we aimed to identify clinical predictors of SARS-CoV-2 infection risk and to develop and validate a score predicting SARS-CoV-2 infection risk comparing it with unspecific surrogates.MethodsRetrospective case/control study using administrative health-related database was carried out in Southern Italy (Campania region) among beneficiaries of Regional Health Service aged over than 30 years. For each subject with Covid-19 confirmed diagnosis (case), up to five controls were randomly matched for gender, age and municipality of residence. Odds ratios and 90% confidence intervals for associations between candidate predictors and risk of infection were estimated by means of conditional logistic regression. SARS-CoV-2 Infection Score (SIS), was developed by generating a total aggregate score obtained from assignment of a weight at each selected covariate using coefficients estimated from the model. Finally, the score was categorized by assigning increasing values from 1 to 4. SIS was validated by comparison with specific and unspecific predictors of SARS-CoV-2 infection.ResultsSubjects suffering from diabetes, anaemias, Parkinson’s disease, mental disorders, cardiovascular and inflammatory bowel and kidney diseases showed increased risk of SARS-CoV-2 infection. Similar estimates were recorded for men and women and younger and older than 65 years. Fifteen conditions significantly contributed to the SIS. As SIS value increases, risk progressively increases, being odds of SARS-CoV-2 infection among people with the highest SIS value (SIS=4), 1.74 times higher than those unaffected by any SIS contributing conditions (SIS=1).ConclusionThis study identified conditions and diseases making individuals more vulnerable to SARS-CoV-2 infection. Our results are a decision-maker support tool for identifying population most at risk allowing adoption of preventive measures to minimize a potential new relapse damage.

show abstract

Drug-utilisation Profiles and COVID-19: Retrospective Cohort Study in Italy

Cited by 4 publications

References 27 publications

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: A population-based cohort study from Italy

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: A population-based cohort study from Italy

Comorbidity in an Older Population with Type-2 Diabetes Mellitus: Identification of the Characteristics and Healthcare Utilization of High-Cost Patients

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy

Contact Info

Product

Resources

About