Gilligan Adrienne M, Skrepnek Grant H
Health Economics and Outcomes Research, Smith & Nephew Biotherapeutics, Fort Worth, TX, USA and The University of Oklahoma Health Sciences Center, College of Pharmacy, Oklahoma City, OK, USA
Health Economics and Outcomes Research, Smith & Nephew Biotherapeutics, Fort Worth, TX, USA and The University of Oklahoma Health Sciences Center, College of Pharmacy, Oklahoma City, OK, USA.
Health Policy Plan. 2015 Jun;30(5):624-37. doi: 10.1093/heapol/czu041. Epub 2014 Jun 11.
Although the Eastern Mediterranean Region (EMR) healthcare sector has been expanding rapidly, many differences exist across socioeconomic status, clinical practice standards and healthcare systems.
Predict production functions of health by measuring socioeconomic and expenditure factors that impact life expectancy in the EMR.
Data from the World Health Organization (WHO) Global Health Observatory and the World Bank were used for this cross-sectional, time-series study spanning 21 nations in the EMR from 1995 to 2010. The primary outcome was life expectancy at birth. Covariates of interest included sociodemographic and health indicators. To both establish and validate appropriate categorization of countries, a cluster analysis was undertaken to group cases by taking selected characteristics into account. A variance-component, multilevel mixed-effects linear model was employed that incorporated a finite, Almon, distributed lag of 5 years and bootstrapping with 5000 simulations to model the production function of life expectancy.
Results of the cluster analysis found four groupings. Clusters 1 and 2, composed of six total countries, generally represented non-industrialized/least developed countries. Clusters 3 and 4, totalling 15 nations, captured more industrialized nations. Overall, gross domestic product (GDP) (P = 0.011), vaccination averages (P = 0.026) and urbanization (P = 0.026), were significant positive predictors of life expectancy. No significant predictors existed for Cluster 1 countries. Among Cluster 2 nations, physician density (P = 0.014) and vaccination averages (P = 0.044) were significant positive predictors. GDP (P = 0.037) and literacy (P = 0.014) were positive significant predictors among Cluster 3 nations. GDP (P = 0.002), health expenditures (P = 0.002) and vaccination averages (P = 0.014) were positive significant predictors in Cluster 4 countries.
Predictors of life expectancy differed between non-industrialized and industrialized nations, with the exception of vaccination averages. Non-industrialized/least developed nations were associated with adjusted life expectancies of >14% lower than their industrialized peers. Continued work to address differences in the quality of and access to care in the EMR is required.
尽管东地中海区域（EMR）的医疗保健部门一直在迅速扩张，但在社会经济地位、临床实践标准和医疗保健系统方面仍存在许多差异。
通过测量影响东地中海区域预期寿命的社会经济和支出因素，预测健康的生产函数。
本横断面时间序列研究使用了世界卫生组织（WHO）全球卫生观测站和世界银行的数据，研究涵盖了1995年至2010年期间东地中海区域的21个国家。主要结果是出生时的预期寿命。感兴趣的协变量包括社会人口统计学和健康指标。为了建立并验证对国家的适当分类，进行了聚类分析，通过考虑选定特征对案例进行分组。采用了方差成分、多层次混合效应线性模型，该模型纳入了一个有限的、阿尔蒙式的5年分布滞后，并进行了5000次模拟的自助法，以模拟预期寿命的生产函数。
聚类分析结果发现了四个分组。第1组和第2组共有6个国家，总体上代表非工业化/最不发达国家。第3组和第4组共有15个国家，涵盖了更多工业化国家。总体而言，国内生产总值（GDP）（P = 0.011）、平均疫苗接种率（P = 0.026）和城市化水平（P = 0.026）是预期寿命的显著正向预测因素。第1组国家没有显著的预测因素。在第2组国家中，医生密度（P = 0.014）和平均疫苗接种率（P = 0.044）是显著的正向预测因素。在第3组国家中，GDP（P = 0.037）和识字率（P = 0.014）是显著的正向预测因素。在第4组国家中，GDP（P = 0.002）、卫生支出（P = 0.002）和平均疫苗接种率（P = 0.014）是显著的正向预测因素。
除了平均疫苗接种率外，非工业化国家和工业化国家的预期寿命预测因素有所不同。非工业化/最不发达国家的调整后预期寿命比工业化国家的同龄人低14%以上。需要继续努力解决东地中海区域在医疗质量和可及性方面的差异。
