Comprehensive and comparable estimates of health spending in each country are a key input for health policy and planning, and are necessary to support the achievement of national and international health goals. Previous studies have tracked past and projected future health spending until 2040 and shown that, with economic development, countries tend to spend more on health per capita, with a decreasing share of spending from development assistance and out-of-pocket sources. We aimed to characterise the past, present, and predicted future of global health spending, with an emphasis on equity in spending across countries. We estimated domestic health spending for 195 countries and territories from 1995 to 2016, split into three categories-government, out-of-pocket, and prepaid private health spending-and estimated development assistance for health (DAH) from 1990 to 2018. We estimated future scenarios of health spending using an ensemble of linear mixed-effects models with time series specifications to project domestic health spending from 2017 through 2050 and DAH from 2019 through 2050. Data were extracted from a broad set of sources tracking health spending and revenue, and were standardised and converted to inflation-adjusted 2018 US dollars. Incomplete or low-quality data were modelled and uncertainty was estimated, leading to a complete data series of total, government, prepaid private, and out-of-pocket health spending, and DAH. Estimates are reported in 2018 US dollars, 2018 purchasing-power parity-adjusted dollars, and as a percentage of gross domestic product. We used demographic decomposition methods to assess a set of factors associated with changes in government health spending between 1995 and 2016 and to examine evidence to support the theory of the health financing transition. We projected two alternative future scenarios based on higher government health spending to assess the potential ability of governments to generate more resources for health. Between 1995 and 2016, health spending grew at a rate of 4·00% (95% uncertainty interval 3·89-4·12) annually, although it grew slower in per capita terms (2·72% [2·61-2·84]) and increased by less than $1 per capita over this period in 22 of 195 countries. The highest annual growth rates in per capita health spending were observed in upper-middle-income countries (5·55% [5·18-5·95]), mainly due to growth in government health spending, and in lower-middle-income countries (3·71% [3·10-4·34]), mainly from DAH. Health spending globally reached $8·0 trillion (7·8-8·1) in 2016 (comprising 8·6% [8·4-8·7] of the global economy and $10·3 trillion [10·1-10·6] in purchasing-power parity-adjusted dollars), with a per capita spending of US$5252 (5184-5319) in high-income countries, $491 (461-524) in upper-middle-income countries, $81 (74-89) in lower-middle-income countries, and $40 (38-43) in low-income countries. In 2016, 0·4% (0·3-0·4) of health spending globally was in low-income countries, despite these countries comprising 10·0% of the global population. In 2018, the largest proportion of DAH targeted HIV/AIDS ($9·5 billion, 24·3% of total DAH), although spending on other infectious diseases (excluding tuberculosis and malaria) grew fastest from 2010 to 2018 (6·27% per year). The leading sources of DAH were the USA and private philanthropy (excluding corporate donations and the Bill & Melinda Gates Foundation). For the first time, we included estimates of China's contribution to DAH ($644·7 million in 2018). Globally, health spending is projected to increase to $15·0 trillion (14·0-16·0) by 2050 (reaching 9·4% [7·6-11·3] of the global economy and $21·3 trillion [19·8-23·1] in purchasing-power parity-adjusted dollars), but at a lower growth rate of 1·84% (1·68-2·02) annually, and with continuing disparities in spending between countries. In 2050, we estimate that 0·6% (0·6-0·7) of health spending will occur in currently low-income countries, despite these countries comprising an estimated 15·7% of the global population by 2050. The ratio between per capita health spending in high-income and low-income countries was 130·2 (122·9-136·9) in 2016 and is projected to remain at similar levels in 2050 (125·9 [113·7-138·1]). The decomposition analysis identified governments' increased prioritisation of the health sector and economic development as the strongest factors associated with increases in government health spending globally. Future government health spending scenarios suggest that, with greater prioritisation of the health sector and increased government spending, health spending per capita could more than double, with greater impacts in countries that currently have the lowest levels of government health spending. Financing for global health has increased steadily over the past two decades and is projected to continue increasing in the future, although at a slower pace of growth and with persistent disparities in per-capita health spending between countries. Out-of-pocket spending is projected to remain substantial outside of high-income countries. Many low-income countries are expected to remain dependent on development assistance, although with greater government spending, larger investments in health are feasible. In the absence of sustained new investments in health, increasing efficiency in health spending is essential to meet global health targets. Bill & Melinda Gates Foundation.

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.

Background Past research has shown how fires, heat and hot substances are important causes of health loss globally. Detailed estimates of the morbidity and mortality from these injuries could help drive preventative measures and improved access to care. Methods We used the Global Burden of Disease 2017 framework to produce three main results. First, we produced results on incidence, prevalence, years lived with disability, deaths, years of life lost and disability-adjusted life years from 1990 to 2017 for 195 countries and territories. Second, we analysed these results to measure mortality-to-incidence ratios by location. Third, we reported the measures above in terms of the cause of fire, heat and hot substances and the types of bodily injuries that result. Results Globally, there were 8 991 468 (7 481 218 to 10 740 897) new fire, heat and hot substance injuries in 2017 with 120 632 (101 630 to 129 383) deaths. At the global level, the age-standardised mortality caused by fire, heat and hot substances significantly declined from 1990 to 2017, but regionally there was variability in age-standardised incidence with some regions experiencing an increase (eg, Southern Latin America) and others experiencing a significant decrease (eg, High-income North America). Conclusions The incidence and mortality of injuries that result from fire, heat and hot substances affect every region of the world but are most concentrated in middle and lower income areas. More resources should be invested in measuring these injuries as well as in improving infrastructure, advancing safety measures and ensuring access to care.

Background: The equality in the distribution of vaccines between and within countries along with follow sanitation tips and observe social distance, are effective strategies to rid the world of COVID-19 pandemic. Inequality in the distribution of COVID-19 vaccine, in addition to causing inequity to the population health, has a significant impact on the process of economic recovery. Methods: All published original papers on the inequality of Covid-19 vaccine distribution and the factors affecting it were searched in PubMed, Web of Science, Scopus and ProQuest databases between December 2020 to 30 May 2022. Selection of articles, extraction of their data and qualitative assessment (by STROBE) were performed by two researchers separately. Data graphing form was used to extract detailed data from each study and then, the collected data were classified. Results: A total of 4623 articles were evaluated. After removing duplicates and screening the title, abstract and full text of articles, 22 articles were selected and entered into the study. Fifteen (68.17%) studies were conducted in the United States, three (13.64%) in Europe, three (13.64%) in Asia and one (6.66%) in Oceania. Factors affecting the inequality in the distribution of COVID-19 vaccine were classified into macro and micro levels determinants. Conclusion: Macro determinants of inequality in the Covid-19 vaccine distribution were consisted of economic (stability and country’s economic status, Gross Domestic Product (GDP) per capita, financial support and human development index), infrastructure and health system (appropriate information system, functional cold chains in vaccine transport, transport infrastructure, medical and non-medical facilities per capita, healthcare access and quality), legal and politics (vaccination allocation rules, health policies, political ideology and racial bias), and epidemiologic and demographic factors (Covid-19 incidence and deaths rate, life expectancy, vulnerability to Covid-19, working in medical setting, comorbidities, social vulnerability, incarceration and education index). Moreover, micro/ individual level factors were included in economic (household’s income, home ownership, employment, poverty, access to healthy food and residency in the deprived areas) and demographic and social characteristics (sex, age, race, ethnic, religion, disability, location (urban/rural) and insurance coverage).

Background Determinants of health or health production function in health economics literature constitute noticeable issues in health promotion. This study aimed at estimating a health production function for East Mediterranean Region (EMR) based on the Grossman theoretical model. Methods This ecological study was performed using the econometric methods. The panel data model was used in order to determine the relationship between life expectancy and socioeconomic factors. The data for 21 EMR countries between 1995 and 2007 were used. Fixed-effect-model was employed to estimate the parameters based on Hausman test. Results In estimating the health production function, factors such as income per capita (β=0.05, P<0.001), education index (β=0.07, P<0.001), food availability (β=0.01, P<0.001), level of urbanisation (β=0.10, P<0.001), and employment ratio (β=0.11, P<0.001) were specified as determinants of health status, proxied by life expectancy at birth. A notable result was the elasticity of life expectancy with respect to the employment rate and its significance level was different between males (β=0.13, P0.001). Conclusion In order to improve the health status in EMR countries, health policymakers should focus on the factors which lie outside the healthcare system. These factors are mainly associated with economic growth and development level. Thus, the economic stabilisation policies with the aim of increasing the productivity, economic growth, and reducing unemployment play significant roles in the health status of the people of the region.

Background: Examining the distribution of the burden of different communicable and non-communicable diseases and injuries worldwide can present proper evidence to global policymakers to deal with health inequality. The present study aimed to determine socioeconomic inequality in the burden of 25 groups of diseases between countries around the world in 2019. Methods: In the current study data according to 204 countries in the world was gathered from the Human Development Report and the Global Burden of Diseases study. Variables referring to incidence, prevalence, years of life lost (YLL), years lived with disability (YLD) and disability adjusted life years (DALY) resulting by 25 groups of diseases and injuries also human development index was applied for the analysis. For measurement of socioeconomic inequality, concentration index (CI) and curve was applied. CI is considered as one of the popular measures for inequality measurement. It ranges from − 1 to + 1. A positive value implies that a variable is concentrated among the higher socioeconomic status population and vice versa. Results: The findings showed that CI of the incidence, prevalence, YLL, YLD and DALY for all causes were − 0.0255, − 0.0035, − 0.1773, 0.0718 and − 0.0973, respectively. CI for total Communicable, Maternal, Neonatal, and Nutritional Diseases (CMNNDs) incidence, prevalence, YLL, YLD and DALY were estimated as − 0.0495, − 0.1355, − 0.5585, − 0.2801 and − 0.5203, respectively. Moreover, estimates indicated that CIs of incidence, prevalence, YLL, YLD and DALY for Non-Communicable Diseases (NCDs) were 0.1488, 0.1218, 0.1552, 0.1847 and 0.1669, respectively. Regarding injuries, the CIs of incidence, prevalence, YLL, YLD and DALY were determined as 0.0212, 0.1364, − 0.1605, 0.1146 and 0.3316, respectively. In the CMNNDs group, highest and lowest CI of DALY were related to the respiratory infections and tuberculosis (− 0.4291) and neglected tropical diseases and malaria (− 0.6872). Regarding NCDs, the highest and lowest CI for DALY is determined for neoplasms (0.3192) and other NCDs (− 0.0784). Moreover, the maximum and minimum of CI of DALY for injuries group were related to the transport injuries (0.0421) and unintentional injuries (− 0.0297). Conclusions: The distribution of all-causes and CMNNDs burden were more concentrated in low-HDI countries and there are pro-poor inequality. However, there is a pro-rich inequality for NCDs’ burden i.e. it was concentrated in high-HDI countries. On the other hand, the concentration of DALY, YLD, prevalence, and incidence in injuries was observed in the countries with higher HDI, while YLL was concentrated in low-HDI countries.