AIMS: To characterize variability of rtPA price, its availability, and its association with and impact on each country's health expenditure (HE) resources.
METHODS: We conducted a global survey to obtain information on rtPA price (50 mg vial, 2020 US Dollars) and availability. Country-specific data, including low, lower middle (LMIC), upper middle (UMIC), and high-income country (HIC) classifications, and gross domestic product (GDP) and HE, both nominally and adjusted for purchasing power parity (PPP), were obtained from World Bank Open Data. To assess the impact of rtPA cost, we computed the rtPA price as percentage of per capita GDP and HE and examined its association with the country income classification.
RESULTS: rtPA is approved and available in 109 countries. We received surveys from 59 countries: 27 (46%) HIC, 20 (34%) UMIC, and 12 (20%) LMIC. Although HIC have significantly higher per capita GDP and HE compared to UMIC and LMIC (p < 0.0001), the median price of rtPA is non-significantly higher in LMICs (USD 755, interquartile range, IQR (575-1300)) compared to UMICs (USD 544, IQR (400-815)) and HICs (USD 600, IQR (526-1000)). In LMIC, rtPA cost accounts for 217.4% (IQR, 27.1-340.6%) of PPP-adjusted per capita HE, compared to 17.6% (IQR (11.2-28.7%), p < 0.0001) for HICs.
CONCLUSION: We documented significant variability in rtPA availability and price among countries. Relative costs are higher in lower income countries, exceeding the available HE. Concerted efforts to improve rtPA affordability in low-income settings are necessary.
METHODS: Based on health stock data from 1990 to 2015 for 140 countries, we estimated Gini coefficients of health stock to investigate associations with a well-known economic flow indicator, Gross Domestic Product (GDP), stock-based national wealth indicator, Inclusive Wealth Index (IWI), and firm-level net income.
RESULTS: The estimated Gini coefficient of global health stock shows that health stock has experienced a global decline. The Gini coefficient for low-income countries (LICs) showed the fastest decline in health stock, dropping from 0.69 to 0.66 in 25 years. Next, rapid population growth and the rise in the youth share of the working-age population in LICs were most likely contributing factors to the decline in inequality. Most countries that experienced positive health stock growth also indicated a strong positive relationship with GDP and IWI. However, some countries showed a negative relationship with natural capital, which is a part of IWI. In addition, firm-level net income showed no obvious associations with health stock, GDP and IWI.
CONCLUSIONS: We argue that a negative relationship between health stock and natural capital is a sign of unstable development because sustainable development involves maintaining not only GDP but also IWI, as it is a collective set of assets or wealth comprising human, produced and natural capital. Moreover, in our analysis of firm-level income data, we also discuss that income will be influenced by other factors, such as innovations, human resources, organization culture and strategy. Therefore, the paper concludes that health stock is a vital component in measuring health inequality and health-related Sustainable Development Goals (SDGs). Thus, IWI is more comprehensive in measuring national wealth and can complement GDP in measuring progress toward sustainable development.
DESIGN: Health efficiency analysis using data envelopment analysis (DEA) and stochastic frontier approach analysis.
SETTING: Health systems in China and ASEAN countries.
METHODS: DEA-Malmquist model and SFA model were used to analyse the health system efficiency among China and ASEAN countries, and the Tobit regression model was employed to analyse the factors affecting the efficiency of health system among these countries.
RESULTS: In 2020, the average technical efficiency, pure technical efficiency and scale efficiency of China and 10 ASEAN countries' health systems were 0.700, 1 and 0.701, respectively. The average total factor productivity (TFP) index of the health systems in 11 countries from 2015 to 2020 was 0.962, with a decrease of 1.4%, among which the average technical efficiency index was 1.016, and the average technical progress efficiency index was 0.947. In the past 6 years, the TFP index of the health system in Malaysia was higher than 1, while the TFP index of other countries was lower than 1. The cost efficiency among China and ASEAN countries was relatively high and stable. The per capita gross domestic product (current US$) and the urban population have significant effects on the efficiency of health systems.
CONCLUSIONS: Health systems inefficiency is existing in China and the majority ASEAN countries. However, the lower/middle-income countries outperformed high-income countries. Technical efficiency is the key to improve the TFP of health systems. It is suggested that China and ASEAN countries should enhance scale efficiency, accelerate technological progress and strengthen regional health cooperation according to their respective situations.
METHODS: This study links three methodologies. First, we estimate the association between physical inactivity and workplace productivity using multivariable regression models with proprietary data on 120 143 individuals in the UK and six Asian countries (Australia, Malaysia, Hong Kong, Thailand, Singapore and Sri Lanka). Second, we analyse the association between physical activity and mortality risk through a meta-regression analysis with data from 74 prior studies with global coverage. Finally, the estimated effects are combined in a computable general equilibrium macroeconomic model to project the economic benefits of physical activity over time.
RESULTS: Doing at least 150 min of moderate-intensity physical activity per week, as per lower limit of the range recommended by the 2020 WHO guidelines, would lead to an increase in global gross domestic product (GDP) of 0.15%-0.24% per year by 2050, worth up to US$314-446 billion per year and US$6.0-8.6 trillion cumulatively over the 30-year projection horizon (in 2019 prices). The results vary by country due to differences in baseline levels of physical activity and GDP per capita.
CONCLUSIONS: Increasing physical activity in the population would lead to reduction in working-age mortality and morbidity and an increase in productivity, particularly through lower presenteeism, leading to substantial economic gains for the global economy.