A case-mix classification system for explaining healthcare costs using administrative data in Italy

Corti, Maria Chiara; Avossa, Francesco; Schievano, Elena; Gallina, Pietro; Ferroni, Eliana; Alba, Natalia; Dotto, M Del; Basso, Cristina; Netti, Silvia Tiozzo; Fedeli, Ugo; Mantoan, Domenico

doi:10.1016/j.ejim.2018.02.035

Cited by 30 publications

(20 citation statements)

References 25 publications

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“…Even age did not have a linear relationship with costs. As shown in a recent Italian study (Corti et al., 2018), our results show that the highest costs are not for the older adults, but for younger patients. However, a recent review showed that higher age was associated with higher costs, but when considering the overall population, half of the high‐cost population were younger than 65 years (Wammes et al., 2018).…”

Section: Discussionsupporting

confidence: 83%

“…Costs are influenced by various factors, such as diagnosis, medical intervention, length of stay, and, consequently, discharge destination (Kwok et al, 2012). Except for specific cases, such as patients with a diagnosis related to the circulatory system that have higher costs (Corti et al, 2018), especially with mild or severe dependence, our study shows a non-significant relationship between BI and cost of hospitalization. Instead, cost of hospitalization seems to be higher for patients with less functional dependence.…”

Section: Discussionmentioning

confidence: 60%

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The Barthel index as an indicator of hospital outcomes: A retrospective cross‐sectional study with healthcare data from older people

Ocagli

Cella

Stivanello

et al. 2020

Journal of Advanced Nursing

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Aims The assessment of functional status is a more appropriate measure in the older people than traditional healthcare outcomes. The present study aimed to analyse the association between functional status assessed using the Barthel Index and length of stay, in‐hospital mortality, discharge destination, and Diagnosis‐Related Groups‐based cost. Design This study was a retrospective study that used administrative data from patients older than 65 discharged from the University Hospital of Padua (Italy) in 2016. Methods A logistic regression model for categorical variables (length of stay, in‐hospital mortality, and discharge destination) and a generalized linear model with gamma distributions and log links for continuous variables (cost of hospitalization) were used to evaluate associations with the Barthel Index. Results A total of 13,484 admissions were included in the analysis. In‐hospital mortality, safe discharge, and length of stay were higher in patients with severe dependence than in patients with mild/no dependence with a 12‐fold increased risk of death (OR = 12.81; 95% CI 9.22–18.14), a 4 times greater likelihood of safe discharge (OR = 4.64; 95% CI 3.96–5.45), and a 2‐fold increase in length of stay (OR = 2.56; 95% CI 2.34–2.81). On the other hand, no significant association was found between the cost of hospitalization and the Barthel Index. Conclusions Barthel Index was strongly associated with in‐hospital mortality, discharge destination, and length of stay. The costs of hospitalization, however, were not related to patients’ functional impairment. Impact The study considers functional status as an indicator of hospital outcomes. Better comprehension of the relationship between functional status and healthcare outcomes may help with early and adequate healthcare planning and resource management.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 60%

The Barthel index as an indicator of hospital outcomes: A retrospective cross‐sectional study with healthcare data from older people

Ocagli

Cella

Stivanello

et al. 2020

Journal of Advanced Nursing

View full text Add to dashboard Cite

show abstract

“…To assure a more complete retrieval of chronic conditions, hospital discharge records were investigated also for the period 2011–2015. By linkage of the above archives, the Johns Hopkins University Adjusted Clinical Groups (ACG) System v.10.0.2 has been adopted to identify patients affected by CLD, using the specific Expanded Diagnosis Clusters (EDC) GAS05 [11,12]. CLD was then classified by aetiology using the following International Classification of Diseases, 9 th Edition-Clinical Modification codes: HBV-related CLD (070.2x, 070.3x, 070.42, 070.52), HCV-related CLD (070.41, 070.44, 070.51, 070.54, 070.7x), alcohol –related CLD (571.0 and 571.3, or CLD in the presence of other alcohol-related diagnoses: 291.x, 303.x, 305.0, 357.5, 425.5, 535.3, 5710, 5711, 5712, 5713, 980.0, V11.3), biliary cirrhosis (571.6).…”

Section: Methodsmentioning

confidence: 99%

Prevalence of chronic liver disease among young/middle-aged adults in Northern Italy: role of hepatitis B and hepatitis C virus infection by age, sex, ethnicity

Fedeli

Avossa

Ferroni

et al. 2019

Heliyon

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Background Sparse population-based data are available on the prevalence and etiology of chronic liver disease (CLD) in Italy. The study aims to assess the role of hepatitis B virus (HBV) and hepatitis C virus (HCV) infection in CLD according to age, gender and ethnicity. Methods Clinically diagnosed CLD in the general population aged 20–59 years in the Veneto Region (North-Eastern Italy) were identified through the Adjusted Clinical Groups System, by record linkage of the archive of subjects enrolled in the Regional Health System with Hospital Discharge Records, Emergency Room visits, Chronic disease registry for copayment exemptions, and the Home care database. Age-standardized prevalence rates (PR) were computed in Italians and immigrants, based on country of citizenship. Results Overall 22,934 subjects affected by CLD in 2016 were retrieved, 21% related to HBV and 43% to HCV infection. The prevalence of HCV-related CLD was higher in males, peaking at 50–54 years (males = 11/1000; females = 4/1000). The PR of HBV-related CLD was almost negligible in the Italian population (1/1000), and higher among immigrants, especially from East Asia (males = 17/1000; females = 11/1000) and Sub-Saharan Africa (males = 13/1000; females = 10/1000). Conclusion Specific population sub-groups identified by age, gender, and ethnicity, were demonstrated to be at increased risk, and these trends are in line with global epidemiological patterns of viral hepatitis.

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“…Using this approach, the risk score represents the healthcare utilization expected to manage the particular combination of clinical conditions experienced by individuals in each cell [ 16 ]. The ACG® System has been shown to explain significantly more variation in utilization than demographics-only models [ 18 , 19 ].…”

Section: Methodsmentioning

confidence: 99%

Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa

Mannie

Kharrazi

2020

BMC Public Health

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Background Comorbidities are strong predictors of current and future healthcare needs and costs; however, comorbidities are not evenly distributed geographically. A growing need has emerged for comorbidity surveillance that can inform decision-making. Comorbidity-derived risk scores are increasingly being used as valuable measures of individual health to describe and explain disease burden in populations. Methods This study assessed the geographical distribution of comorbidity and its associated financial implications among commercially insured individuals in South Africa (SA). A retrospective, cross-sectional analysis was performed comparing the geographical distribution of comorbidities for 2.6 million commercially insured individuals over 2016–2017, stratified by geographical districts in SA. We applied the Johns Hopkins ACG® System across the insurance claims data of a large health plan administrator in SA to measure comorbidity as a risk score for each individual. We aggregated individual risk scores to determine the average risk score per district, also known as the comorbidity index (CMI), to describe the overall disease burden of each district. Results We observed consistently high CMI scores in districts of the Free State and KwaZulu-Natal provinces for all population groups before and after age adjustment. Some areas exhibited almost 30% higher healthcare utilization after age adjustment. Districts in the Northern Cape and Limpopo provinces had the lowest CMI scores with 40% lower than expected healthcare utilization in some areas after age adjustment. Conclusions Our results show underlying disparities in CMI at national, provincial, and district levels. Use of geo-level CMI scores, along with other social data affecting health outcomes, can enable public health departments to improve the management of disease burdens locally and nationally. Our results could also improve the identification of underserved individuals, hence bridging the gap between public health and population health management efforts.

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A case-mix classification system for explaining healthcare costs using administrative data in Italy

Cited by 30 publications

References 25 publications

The Barthel index as an indicator of hospital outcomes: A retrospective cross‐sectional study with healthcare data from older people

The Barthel index as an indicator of hospital outcomes: A retrospective cross‐sectional study with healthcare data from older people

Prevalence of chronic liver disease among young/middle-aged adults in Northern Italy: role of hepatitis B and hepatitis C virus infection by age, sex, ethnicity

Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa

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