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Mutie FM, Mbuni YM, Rono PC, Mkala EM, Nzei JM, Phumthum M, Hu GW, Wang QF. Important Medicinal and Food Taxa (Orders and Families) in Kenya, Based on Three Quantitative Approaches. Plants (Basel) 2023; 12:1145. [PMID: 36904005 PMCID: PMC10005506 DOI: 10.3390/plants12051145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 02/25/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Globally, food and medicinal plants have been documented, but their use patterns are poorly understood. Useful plants are non-random subsets of flora, prioritizing certain taxa. This study evaluates orders and families prioritized for medicine and food in Kenya, using three statistical models: Regression, Binomial, and Bayesian approaches. An extensive literature search was conducted to gather information on indigenous flora, medicinal and food plants. Regression residuals, obtained using LlNEST linear regression function, were used to quantify if taxa had unexpectedly high number of useful species relative to the overall proportion in the flora. Bayesian analysis, performed using BETA.INV function, was used to obtain superior and inferior 95% probability credible intervals for the whole flora and for all taxa. To test for the significance of individual taxa departure from the expected number, binomial analysis using BINOMDIST function was performed to obtain p-values for all taxa. The three models identified 14 positive outlier medicinal orders, all with significant values (p < 0.05). Fabales had the highest (66.16) regression residuals, while Sapindales had the highest (1.1605) R-value. Thirty-eight positive outlier medicinal families were identified; 34 were significant outliers (p < 0.05). Rutaceae (1.6808) had the highest R-value, while Fabaceae had the highest regression residuals (63.2). Sixteen positive outlier food orders were recovered; 13 were significant outliers (p < 0.05). Gentianales (45.27) had the highest regression residuals, while Sapindales (2.3654) had the highest R-value. Forty-two positive outlier food families were recovered by the three models; 30 were significant outliers (p < 0.05). Anacardiaceae (5.163) had the highest R-value, while Fabaceae had the highest (28.72) regression residuals. This study presents important medicinal and food taxa in Kenya, and adds useful data for global comparisons.
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Affiliation(s)
- Fredrick Munyao Mutie
- CAS Key Laboratory of Plant Germplasm and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Peninah Cheptoo Rono
- CAS Key Laboratory of Plant Germplasm and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Elijah Mbandi Mkala
- CAS Key Laboratory of Plant Germplasm and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - John Mulinge Nzei
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Methee Phumthum
- Department of Pharmaceutical Botany, Faculty of Pharmacy, Mahidol University, Bangkok 10400, Thailand
| | - Guang-Wan Hu
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qing-Feng Wang
- CAS Key Laboratory of Plant Germplasm and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Ortega-Díaz MI, Ocaña-Riola R, Pérez-Romero C, Martín-Martín JJ. Multilevel Analysis of the Relationship between Ownership Structure and Technical Efficiency Frontier in the Spanish National Health System Hospitals. Int J Environ Res Public Health 2020; 17:ijerph17165905. [PMID: 32823922 PMCID: PMC7459985 DOI: 10.3390/ijerph17165905] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/08/2020] [Accepted: 08/11/2020] [Indexed: 11/29/2022]
Abstract
Objective: To evaluate the relationship between the ownership structure of hospitals and the possibility of their being positioned on the frontier of technical efficiency in the economic crisis period 2010–2012, adjusting for hospital variables and regional characteristics in the areas where the Spanish National Health System (SNHS) hospitals are located. Methods: 230 National Health System hospitals were studied over the two-year period 2010–2012 according to their ownership structure—public hospitals, private hospitals and public–private partnership (PPP)—data envelopment analysis orientated to inputs was used to measure the overall technical efficiency, pure efficiency and efficiency of scale. A generalised linear mixed model (GLMM) with binomial distribution and logit link function was used to analyse the hospital and regional variables associated with positioning on the frontier. Results: There are substantial differences between the average pure technical efficiency of public, private and PPP hospitals, as well as a greater number of PPP models being positioned on the efficiency frontier (91.67% in 2012). The odds of being positioned on the frontier are 41.7 times higher in PPP models than in public hospitals. The average annual household income per region is related to the greater odds of hospitals being positioned on the frontier of efficiency. Conclusions: During the most acute period of recession in the Spanish economy, PPP formulas favoured hospital efficiency, by increasing the odds of being positioned on the frontier of efficiency when compared to private and public hospitals. The position on the frontier of efficiency of a hospital is related to the wealth of its region.
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Affiliation(s)
- Mª Isabel Ortega-Díaz
- Departamento de Economía, Universidad de Jaén, Edificio D-3, Campus Las Lagunillas s/n, 23071 Jaén, Spain;
| | - Ricardo Ocaña-Riola
- Escuela Andaluza de Salud Pública, Cuesta del Observatorio 4, Campus Universitario de Cartuja, 18011 Granada, Spain;
- Instituto de Investigación Biosanitaria ibs.GRANADA, Doctor Azpitarte 4, 18012 Granada, Spain;
| | - Carmen Pérez-Romero
- Escuela Andaluza de Salud Pública, Cuesta del Observatorio 4, Campus Universitario de Cartuja, 18011 Granada, Spain;
- Correspondence: ; Tel.: +34-958-02-74-10
| | - José Jesús Martín-Martín
- Instituto de Investigación Biosanitaria ibs.GRANADA, Doctor Azpitarte 4, 18012 Granada, Spain;
- Departamento de Economía Aplicada, Universidad de Granada, Facultad de Ciencias Económicas y Empresariales, Campus Universitario de Cartuja s/n, 18071 Granada, Spain
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Abstract
Throughout the world, surveys have been conducted at the country level to answer research questions pertaining to ethnomedicinal usage patterns. This study is focused on Thailand, which has never been surveyed systematically in this way. We mined 16,000 records of medicinal plant use from 64 scientific reports, which were published from 1990 to 2014. In total, 2,187 plant species were cited as being useful for medicinal purposes. The overall aim was to reveal the relative importance of the plant families for pharmacological research. To determine the most important medicinal plant families, we use a combination of three statistical approaches: linear regression, Binomial analysis, and Bayesian analysis. At the regional level, 19 plant families repeatedly stood out as being the most important from an ethnomedicinal perspective.
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Affiliation(s)
- Methee Phumthum
- Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Aarhus, Denmark
| | - Henrik Balslev
- Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Aarhus, Denmark
| | - Anders S Barfod
- Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Aarhus, Denmark
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