Predicting healthcare expenditure by multimorbidity groups

Abstract

This article has two main purposes. Firstly, to model the integrated healthcare expenditure for the entire population of a health district in Spain, according to multimorbidity, using Clinical Risk Groups (CRG). Secondly, to show how the predictive model is applied to the allocation of health budgets. MethodsThe database used contains the information of 156,811 inhabitants in a Valencian Community health district in 2013. The variables were: age, sex, CRG's main health statuses, severity level, and healthcare expenditure. The two-part models were used or predicting healthcare expenditure. From the coefficients of the selected model, the relative weights of each group were calculated to set a case-mix in each health district. ResultsModels based on multimorbidity-related variables better explained integrated healthcare expenditure.In the first part of the two-part models, a logit model was used, while the positive costs were modelled with a log-linear OLS regression. An adjusted R 2 of 46-49% between actual and predicted values was obtained. With the weights obtained by CRG, the differences found with the case-mix of each health district proved most useful for budgetary purposes. ConclusionsThe expenditure models allowed improved budget allocations between health districts by taking into account morbidity, as opposed to budgeting based solely on population size.

Health and Housing Energy Expenditures: A Two-Part Model Approach

“…The second and third choices are explained below and correspond to the GLM model family and link choices. Similar to prior studies [42,61,63], this study has opted for a logit alternative to model the probability of having expenditures greater than zero, as defined by Equation ( 2). This option was based on the qualities of simplicity and interpretability the logit model presents in comparison to probit alternative [64].…”

“…Additionally, specification tests for independent variables such as Pregribon and Ramsey RESET tests were also conducted. This procedure has been applied previously by several authors (see [40,42,63]) to detect misspecification by omitted variables. Performed tests were non-significant, meaning that the model is well specified and does not need additional variables.…”

“…Taking into consideration socioeconomic conditions, health status, health risks and health care use as explanatory variables, the two-part model established that the prevalence of excessive weight and obesity contributed to an increase in the probability of household health expenditures [41]. More recently, a two-part model was also developed by Caballer-Tarazona et al [42] to model the integrated healthcare expenditure for the entire population of a health district in Spain. The author was able to develop improved expenditure models that account for multimorbidity, including socioeconomic variables, such as age, gender, health status severity level and healthcare expenditures.…”

Data Envelopment Analysis Applications on Primary Health Care Using Exogenous Variables and Health Outcomes

“…Among the input and output variables, the one that most influences achieving the efficient frontier, together with human resources, is pharmaceutical expenditure, which represents the greatest cost for the BHUs (around 60% of the total cost of the primary healthcare centres, as also suggested by similar studies [39,40]). Among the inefficient units, there is a need to reduce pharmaceutical expenditure by between 93,415 and 751,928 Euros per 10,000 inhabitants, which entails, on average, a reduction of over 20%.…”

Systematic review on the instruments used for measuring the association of the level of multimorbidity and clinically important outcomes

“…Hospitalisation (1 year), 80 mortality (3 years), 48 number of primary care consultations (3 years), 48 primary healthcare cost, 47 readmission within 30 days (1 year) 21 ADG Hospitalisation (1 year), 107 number of primary care physician visits (1 year), 107 number of specialist visits (1 year) 107 CRG Healthcare expenditure, 49 HRQoL using EQ-5D-3L 87 Adjusted Morbidity Groups (GMA) Use of healthcare resources 88 HM Emergency department visits (1 year), 21 healthcare expenditure, 21 hospitalisation (1 year), 21 readmission within 30 days (1 year) 21 HSMI Healthcare cost (primary care) 78 Minnesota Tiering Emergency department visits (1 year), 21 114 healthcare expenditure, 21 hospitalisation (1 year), 21 114 readmission within 30 days (1 year) 21 Resource Utilisation Band Fee-for-service expenditures, 77 primary healthcare cost, 47 mortality (3 years), 48 number of primary care consultations (3 years), 48 registered active listing in primary care, 100 registered active listing in all healthcare 100 CDS Hospitalisation (1 year), 99 mortality (1 year) 99 Drug Count Emergency hospital admission (2 years), 120 functional decline (2 years), 120 hospitalisation (1 year), 99 mortality (1 year), 99 (3 years), 48 number of primary care consultations (3 years) 48 Pra tool Modified using RxRisk-V Emergency hospital admission (1 year) 119 RxRisk-V Emergency hospital admission (2 years), 120 functional decline (2 years) 120…”