BACKGROUND: Little is known on the efficiency of mental health care in small health areas (SHA) particularly where care is organised in sectors and information is incomplete. AIMS OF THE STUDY: To develop an operational model of basic mental health community care (B-MHCC). and to use it to assess the technical efficiency of small health areas. METHOD: A hybrid qualitative and quantitative method was followed to develop a B-MHCC model based on 12 indicators of residential and day care availability and utilisation. It was used for providing qualitative ratings of 12 SHA and to design an standard weighted input-oriented variable returns to scale-Data Envelopment Analysis (DEA) to assess the technical efficiency in each area with a maximum discrimination. The agreement between both strategies was tested via Intraclass Correlation Coefficient (ICC) and predictive validity. RESULTS: One SHA was classified as efficient (benchmark) and seven as inefficient by experts. A DEA model based on two input and 4 output variables rated 6 areas as inefficient, five of these coincided with the experts' opinion (ICC: 0.87). The improvements which every inefficient area required to reach efficiency were also estimated. DISCUSSION: The expert-driven model of community care (B-MHCC) is appropriate to test technical efficiency in Spain. The DEA model, combined with the qualitative rating, proved more useful to detect inefficiency than efficiency. It also provided the size of improvements needed to reach efficiency. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: This model may help decision makers to adjust resource allocation as well as to monitor the efficiency changes over time within small health areas. However, further statistical correlation analysis must be carried out to determine parameter relationships and new mathematical parameter transformations must also be carried out to improve DEA results, as we cannot state that the model will provide similar results when applied to other SHA. IMPLICATIONS FOR HEALTH POLICIES: The DEA model can be applied to care decision, planning and benchmarking. This approach could be relevant for health planning in countries where mental healthcare is organised in sectors, particularly when the available information on service availability and use is not comprehensive.
BACKGROUND: Little is known on the efficiency of mental health care in small health areas (SHA) particularly where care is organised in sectors and information is incomplete. AIMS OF THE STUDY: To develop an operational model of basic mental health community care (B-MHCC). and to use it to assess the technical efficiency of small health areas. METHOD: A hybrid qualitative and quantitative method was followed to develop a B-MHCC model based on 12 indicators of residential and day care availability and utilisation. It was used for providing qualitative ratings of 12 SHA and to design an standard weighted input-oriented variable returns to scale-Data Envelopment Analysis (DEA) to assess the technical efficiency in each area with a maximum discrimination. The agreement between both strategies was tested via Intraclass Correlation Coefficient (ICC) and predictive validity. RESULTS: One SHA was classified as efficient (benchmark) and seven as inefficient by experts. A DEA model based on two input and 4 output variables rated 6 areas as inefficient, five of these coincided with the experts' opinion (ICC: 0.87). The improvements which every inefficient area required to reach efficiency were also estimated. DISCUSSION: The expert-driven model of community care (B-MHCC) is appropriate to test technical efficiency in Spain. The DEA model, combined with the qualitative rating, proved more useful to detect inefficiency than efficiency. It also provided the size of improvements needed to reach efficiency. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: This model may help decision makers to adjust resource allocation as well as to monitor the efficiency changes over time within small health areas. However, further statistical correlation analysis must be carried out to determine parameter relationships and new mathematical parameter transformations must also be carried out to improve DEA results, as we cannot state that the model will provide similar results when applied to other SHA. IMPLICATIONS FOR HEALTH POLICIES: The DEA model can be applied to care decision, planning and benchmarking. This approach could be relevant for health planning in countries where mental healthcare is organised in sectors, particularly when the available information on service availability and use is not comprehensive.
Authors: Jose A Salinas-Perez; Luis Salvador-Carulla; Sandra Saldivia; Pamela Grandon; Alberto Minoletti; Cristina Romero Lopez-Alberca Journal: Rev Panam Salud Publica Date: 2018-10-18
Authors: Nerea Almeda; Carlos R García-Alonso; José A Salinas-Pérez; Mencía R Gutiérrez-Colosía; Luis Salvador-Carulla Journal: Int J Environ Res Public Health Date: 2019-01-25 Impact factor: 3.390
Authors: Carlos R García-Alonso; Nerea Almeda; José A Salinas-Pérez; Mencía R Gutiérrez-Colosía; José J Uriarte-Uriarte; Luis Salvador-Carulla Journal: PLoS One Date: 2019-02-14 Impact factor: 3.240
Authors: Carlos R García-Alonso; Nerea Almeda; José A Salinas-Pérez; Mencía R Gutiérrez-Colosía; Álvaro Iruin-Sanz; Luis Salvador-Carulla Journal: PLoS One Date: 2022-03-22 Impact factor: 3.240
Authors: Nerea Almeda; Carlos Ramón García-Alonso; Helen Killaspy; Mencía R Gutiérrez-Colosía; Luis Salvador-Carulla Journal: PLoS One Date: 2022-03-17 Impact factor: 3.240
Authors: Younjin Chung; Luis Salvador-Carulla; José A Salinas-Pérez; Jose J Uriarte-Uriarte; Alvaro Iruin-Sanz; Carlos R García-Alonso Journal: Health Res Policy Syst Date: 2018-04-25