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. 2022 Jun 21;19(13):7553.
doi: 10.3390/ijerph19137553.

Healthcare Burden of Rare Diseases: A Population-Based Study in Tuscany (Italy)

Silvia Baldacci  1 , Michele Santoro  1 , Anna Pierini  1   2 , Lorena Mezzasalma  1 , Francesca Gorini  1 , Alessio Coi  1
Affiliations

Healthcare Burden of Rare Diseases: A Population-Based Study in Tuscany (Italy)

Silvia Baldacci et al. Int J Environ Res Public Health. .

Abstract

Patients with rare diseases (RDs) need tailored, continuous, and multidisciplinary hospital care. This retrospective cohort study aimed to analyse the healthcare burden of RD patients using a multi-database approach, by linking the data of the Rare Diseases Registry of Tuscany with the regional hospital discharge database. The study population included 21,354 patients diagnosed with a RD between 1 January 2000 and 31 December 2017. The healthcare burden was evaluated for all the RDs during 2009-2018 period. The hospitalisation rate (per 1000) decreased over the years, ranging from 606.9 in 2009 (95% CI: 589.2-625.0) to 443.0 in 2018 (95% CI: 433.2-453.0). A decrease in the average length of stay (LOS) was observed in the earlier years, followed by an increase up to a steady trend (8.3 days in 2018). The patients with RDs of metabolism and the genitourinary system showed the highest hospitalisation rate (903.3 and 644.0 per 1000, respectively). The patients with rare immune system disorders and diseases of the skin and subcutaneous tissue showed the highest LOS (9.7 and 9.5 days, respectively). The methodological approach presented in this population-based study makes it possible to estimate the healthcare burden of RDs, which is crucial in the decision-making and planning aimed at improving patient care.

Keywords: data linkage; disease registry; hospitalisation; length of stay; rare diseases.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the selection process.
Figure 2
Figure 2
Hospitalisation time trend, overall and by type of admission.
Figure 3
Figure 3
Time trend of proportion of inpatients by sex.
Figure 4
Figure 4
Time trend of average length of stay (expressed in days) in hospital.
Figure 5
Figure 5
(a,b) Time trend of main causes of hospitalisation by ICD-9-CM group.
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