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The effect of quality and socio-demographic variables on efficiency measures in primary health care

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Abstract

This paper aims to extend the literature on measuring efficiency in primary health care by considering the influence of quality indicators and environmental variables conjointly in a case study. In particular, environmental variables are represented by patients’ characteristics and quality indicators are based on technical aspects. In order to deal with both aspects, different extensions of data envelopment analysis (DEA) methodology are applied. Specifically, we use weight restrictions to ensure that the efficiency scores assigned to the evaluated units take quality data into account, and a four-stage model to identify which exogenous variables have impact on performance as well as to compute efficiency scores that incorporate this information explicitly. The results provide evidence in support of the importance of including information about both aspects in the analysis so that the efficiency measures obtained can be interpreted as an accurate reflection of performance.

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Notes

  1. An exception is Steinmann et al. [5], who provide a comprehensive review of efficiency studies in both hospitals and primary care services.

  2. See [6–8] for an overview of studies on the measurement of efficiency in the health sector.

  3. See [26, 27] for details.

  4. Our purpose is limited to provide practitioners and decision makers with a useful tool to perform an efficiency analysis of primary health care centres considering quality and quantitative indicators jointly. The setting of the exact value of weight restrictions should therefore be undertaken by highly qualified experts in order to preserve equity and fairness in the evaluation.

  5. Slacks are potential improvements for inefficient units that are not accounted for in the DEA (radial) score. See Toffallis [36] for an illustrated definition of slacks.

  6. The total number of PCCs in 2006 was 103, but 6 of them were excluded due to having missing data, while 3 were considered as outliers by the method developed by Johnson and McGinnis [40]. The main advantage of this method is that it fits better to the multi-stage approach adopted in our empirical analysis, since it avoids potential distortions that could arise if there are outliers among the units exhibiting particularly poor performance.

  7. The health targets include the following items: population aged 0–14 entered in the register of vaccinations and correctly vaccinated; population aged over 15 entered in the register of adult vaccinations and correctly vaccinated against tetanus; population belonging to a risk group registered and correctly vaccinated against hepatitis B; population aged over 64 vaccinated against influenza; population aged 0–14 included in the dental health program; and female population included in the health program for early diagnosis of breast cancer.

  8. Specifically, the questionnaire contains ten questions inquiring about the following items: the existence of a training plan for doctors, a stock list of medicines, an organisation chart in the centre, a monitoring plan for economic imbalances, a maintenance plan for the physical facilities, an informative guide about the services provided, accessibility for people with disabilities, a survey questionnaire designed to obtain information about the level of membership of medical staff in scientific associations and regular attendance of medical staff at conferences.

  9. Adler and Yazhemsky [46] use a Monte Carlo simulation to compare the performance of the PCA approach with the variable reduction and demonstrate that the former provides more accurate results.

  10. See [47, 48] for a detailed explanation of the procedure.

  11. Following Banker [50], we tested the data for scale effects, finding that there are units operating at different scales. Moreover, the VRS assumption facilitates comparison with the constrained model, for which the status of returns to scale may undergo a change when weight restrictions are added [51].

  12. Efficiency scores have been calculated using DEAP 2.1 software [52], which also estimates radial and non-radial slacks for each variable using a multi-stage process.

  13. Table 5 reports the Spearman correlation coefficients among all the models specified and calculated in this study.

  14. The correlation coefficients between all the pairs of these models are greater than 0.93 in every case (see Table 5).

  15. We performed 1,000 iterations.

  16. In this procedure, we take into account only those variables that have a significant effect on each slack.

  17. Here the values of the population density variable (DENSITY) are the greatest of the entire sample and the values of ER, AGRIEMP and DR are notably lower than in rural areas.

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Acknowledgments

The authors are grateful to the Consejería de Sanidad y Dependencia of the Junta de Extremadura for financial support and for making data available to us. We also thank Marcelino Sánchez and Ana Godoy for their assistance in preparing the dataset, and two anonymous referees for their helpful comments and suggestions.

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  1. Department of Economics, University of Extremadura, Av. Elvas s/n, 06071, Badajoz, Spain

    José Manuel Cordero Ferrera, Eva Crespo Cebada & Luis R. Murillo Zamorano

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  1. José Manuel Cordero Ferrera
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Correspondence to José Manuel Cordero Ferrera.

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Cordero Ferrera, J.M., Cebada, E.C. & Murillo Zamorano, L.R. The effect of quality and socio-demographic variables on efficiency measures in primary health care. Eur J Health Econ 15, 289–302 (2014). https://doi.org/10.1007/s10198-013-0476-1

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