Additional file 2: of A data infrastructure for the assessment of health care performance: lessons from the BRIDGE-health project

Annex 2. (PDF 260 kb

Descriptive statistics and figures.

IntroductionThe sustainability of public hospital financing in Spain is a recurring issue, given its representativeness in annual public healthcare budgets which must adapt to the macroeconomic challenges that influence the evolution of spending. Knowing whether the responsiveness of hospital expenditure to its determinants (need, utilisation, and quasi-prices) varies according to the type of hospital could help better design strategies aimed at optimising performance.MethodsUsing SARIMAX models, we dynamically assess unique nationwide monthly activity data over a 14-year period from 274 acute-care hospitals in the Spanish National Health Service network, clustering these providers according to the average severity of the episodes treated.ResultsAll groups showed seasonal patterns and increasing trends in the evolution of expenditure. The fourth quartile of hospitals, treating the most severe episodes and accounting for more than 50% of expenditure, is the most sensitive to quasi-price factors, particularly the number of beds per hospital. Meanwhile, the first quartile of hospitals, which treat the least severe episodes and account for 10% of expenditure, is most sensitive to quantity factors, for which expenditure showed an elasticity above one, while factors of production were not affected.ConclusionsBelonging to one or another cluster of hospitals means that the determinants of expenditure have a different impact and intensity. The system should focus on these differences in order to optimally modulate expenditure not only according to the needs of the population, but also according to the macroeconomic situation, while leaving hospitals room for manoeuvre in case of unforeseen events. The findings suggest strengthening a network of smaller hospitals (Group 1)–closer to their reference population, focused on managing and responding to chronicity and stabilising acute events–prior to transfer to tertiary hospitals (Group 4)–larger but appropriately sized, specialising in solving acute and complex health problems–when needed.</div

Evolution of monthly deflated need-adjusted public hospital expenditure by group of hospitals.

Hospitals are clustered according to the average complexity of the episodes they attended to. Group 1 includes hospitals that treat the least complex cases on average, up to Group 4, which includes hospitals that treat the most complex episodes of care. The overlapping line “MA12centred” stands for the 12th-order centred moving average of hospital expenditure.</p

Definition of the classification quadrants.

<p>Definition of the classification quadrants.</p

Observed to predicted need-adjusted public expenditure interannual growth in hospital care.

Goodness of fit of models: Cgr1- corrected R2 = 0.9886, model Cgr2- corrected R2 = 0.9928, model Cgr3—corrected R2 = 0.9930, model Cgr4—corrected R2 = 0.9925).</p

Impact on public hospital expenditure, by subgroup of hospitals, according to 1% variation in one of the specified determinants.

Impact on public hospital expenditure, by subgroup of hospitals, according to 1% variation in one of the specified determinants.</p

Inputs, outputs and quality indicators. Descriptive statistics.

<p>Inputs, outputs and quality indicators. Descriptive statistics.</p

Dynamic joint performance assessment.

<p>Each bubble represents a hospital's outcome, blue bubbles account for 2003 figures and orange bubbles for 2013. Bubble size is related to the amount of functioning beds at each hospital. Lines placed at median values of TE and LQ delimits hospital's relative position. Low-quality (LQ) is measured in terms of per thousand patient at risk.</p

Deflated, severity adjusted, public hospital expenditure by subgroup of hospitals according to the severity of the episodes attended to (re-parametrised coef.).

Deflated, severity adjusted, public hospital expenditure by subgroup of hospitals according to the severity of the episodes attended to (re-parametrised coef.).</p

Hospitalization risk maps of the spatial region effect -upper row, exp(<i>v</i>_<i>j</i>)- and of the global spatial component -middle row, exp(<i>v</i>_<i>j</i> + <i>u</i>_{<i>i</i>(<i>j</i>)})- for Percutaneous Coronary Intervention (PCI; left), Colectomy in Colorectal Cancer (CCC; middle) and Chronic Obstructive Pulmonary Disease (COPD, right).

<p>At the bottom row, the average temporal trend exp (<i>γ</i>_<i>t</i>) (2002–2013).</p