Search

Abstract Background Primary healthcare management efficiency conditions the functioning of specialized care and has a direct impact on the outcomes of the health system and its sustainability. The objective of this research is to develop models to evaluate the efficiency, including health outcomes, of the primary healthcare centres (PHC) of the Clínico – La Malvarrosa Health District in Valencia. Methods To evaluate efficiency, Data Envelopment Analysis (DEA) was used with output orientation and variable returns to scale, with panel data from the years 2015 to 2019. In rates per 10,000 inhabitants, the inputs are: medical and nursing staff and pharmacy cost. The outputs are: number of consultations, hospital emergencies, referrals, avoidable hospitalisations, avoidable mortality and pharmaceutical prescription efficiency. As exogenous variables: the percentage of population over 65 years old, over 80 and case-mix. Three models were developed, all of them with the same inputs and different combinations of outputs related to: healthcare activity, outcomes, and both, in order to study the influence of the different approaches on efficiency. Each model is analysed both without exogenous variables and with each of them. Results The efficiency results vary depending on the model used, although certain PHCs are always on, or very close to, the efficient frontier, while others are always inefficient. When healthcare activity outputs are considered, efficiency scores improve and the number of efficient PHCs increases. However, in general, the PHC score decreases throughout the evaluated period. This decrease is more pronounced when only activity outputs are included. Conclusions DEA allows the inefficiencies of PHCs to be analysed and the efficient ones are clearly distinguished from the inefficient, although different efficiency scores are obtained depending on the model used. Evaluation can be according to healthcare activity, health outcomes or both, making it necessary to identify the expected objectives of the PHCs, as the perspective of the analysis influences the results.

Abstract Background Increased life expectancy in Western societies does not necessarily mean better quality of life. To improve resources management, management systems have been set up in health systems to stratify patients according to morbidity, such as Clinical Risk Groups (CRG). The main objective of this study was to evaluate the effect of multimorbidity on health-related quality of life (HRQL) in primary care. Methods An observational cross-sectional study, based on a representative random sample (n = 306) of adults from a health district (N = 32,667) in east Spain (Valencian Community), was conducted in 2013. Multimorbidity was measured by stratifying the population with the CRG system into nine mean health statuses (MHS). HRQL was assessed by EQ-5D dimensions and the EQ Visual Analogue Scale (EQ VAS). The effect of the CRG system, age and gender on the utility value and VAS was analysed by multiple linear regression. A predictive analysis was run by binary logistic regression with all the sample groups classified according to the CRG system into the five HRQL dimensions by taking the “healthy” group as a reference. Multivariate logistic regression studied the joint influence of the nine CRG system MHS, age and gender on the five EQ-5D dimensions. Results Of the 306 subjects, 165 were female (mean age of 53). The most affected dimension was pain/discomfort (53%), followed by anxiety/depression (42%). The EQ-5D utility value and EQ VAS progressively lowered for the MHS with higher morbidity, except for MHS 6, more affected in the five dimensions, save self-care, which exceeded MHS 7 patients who were older, and MHS 8 and 9 patients, whose condition was more serious. The CRG system alone was the variable that best explained health problems in HRQL with 17%, which rose to 21% when associated with female gender. Age explained only 4%. Conclusions This work demonstrates that the multimorbidity groups obtained by the CRG classification system can be used as an overall indicator of HRQL. These utility values can be employed for health policy decisions based on cost-effectiveness to estimate incremental quality-adjusted life years (QALY) with routinely e-health data. Patients under 65 years with multimorbidity perceived worse HRQL than older patients or disease severity. Knowledge of multimorbidity with a stronger impact can help primary healthcare doctors to pay attention to these population groups.