Assessment of Primary Care Performance in Luxembourg


CALL: 2019

DOMAIN: BM - Public Health






KEYWORDS: Primary care, performance assessment, technical efficiency, ambulatory care sensitive conditions, fixed and random effects, multi-level models, propensity score matching, difference-in differences, expert interviews, health care utilisation.

START: 2020-01-01



Submitted Abstract

Primary care can ensure continuity and coordination of care, and contribute to health promotion and disease prevention. It has the potential to improve health outcomes, health system efficiency and health equity. Systematic performance assessment of primary care across countries can help to achieve these objectives by identifying areas for improvement and informing policy solutions. We undertake an in-depth assessment of the performance of primary care in Luxembourg in comparison with other European countries, bearing in mind the limitations of available data and the challenges of measuring performance. We move beyond current evidence and make a valuable contribution in two ways. First, we exploit rich and under-utilised data sources to investigate performance. Second, we apply state-of-the-art methodology to these data to produce high-quality evidence to inform improvements in performance. The project is comprised of three main work packages (WPs). In WP1, we investigate performance in terms of the technical efficiency (i.e. the ability to produce the maximum output from a given level of inputs) of primary care across European countries using country-level data. We estimate technical efficiency using parametric (Stochastic Frontier Analysis) and non-parametric (Data Envelopment Analysis) approaches. In WP2, we investigate performance regarding access, coordination, efficiency and quality. We use individual- and country-level data to investigate variation in GP consultations and the utilisation of inpatient care for selected conditions for which timely and effective ambulatory care could prevent hospital admission. We apply multi-level random effects and two-stage fixed effects models to these data. In WP3, we evaluate a distinct primary care policy in Luxembourg – the Médecin Référent (Referring Doctor) programme, which we expect to improve performance on the efficiency and quality of care. We apply propensity score matching and difference-in-differences analyses to individual-level administrative data. We complement these analyses with expert interviews to provide contextual information and to interpret the quantitative results. By undertaking a comprehensive performance assessment of primary care in Luxembourg, this project addresses an important research gap identified by the Luxembourg National Research Fund.

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