Home-Based Work and Home-Based Workers (1800-2021)

Home-Based Work and Home-Based Workers (1800-2021) is about the past and present of home-based work and homebased workers between 1800 and 2021 from a global perspective.; Readership: All interested in social and economic history, and especially in the past and present of home-based work and homebased workers

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background: Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods: We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings: From 1950 to 2017, TFRs decreased by 49\ub74% (95% uncertainty interval [UI] 46\ub74–52\ub70). The TFR decreased from 4\ub77 livebirths (4\ub75–4\ub79) to 2\ub74 livebirths (2\ub72–2\ub75), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83\ub78 million people per year since 1985. The global population increased by 197\ub72% (193\ub73–200\ub78) since 1950, from 2\ub76 billion (2\ub75–2\ub76) to 7\ub76 billion (7\ub74–7\ub79) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2\ub70%; this rate then remained nearly constant until 1970 and then decreased to 1\ub71% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2\ub75% in 1963 to 0\ub77% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2\ub77%. The global average age increased from 26\ub76 years in 1950 to 32\ub71 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59\ub79% to 65\ub73%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1\ub70 livebirths (95% UI 0\ub79–1\ub72) in Cyprus to a high of 7\ub71 livebirths (6\ub78–7\ub74) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0\ub708 livebirths (0\ub707–0\ub709) in South Korea to 2\ub74 livebirths (2\ub72–2\ub76) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0\ub73 livebirths (0\ub73–0\ub74) in Puerto Rico to a high of 3\ub71 livebirths (3\ub70–3\ub72) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2\ub70% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Funding: Bill & Melinda Gates Foundation

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background: Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods: We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings: From 1950 to 2017, TFRs decreased by 49·4% (95% uncertainty interval [UI] 46·4–52·0). The TFR decreased from 4·7 livebirths (4·5–4·9) to 2·4 livebirths (2·2–2·5), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83·8 million people per year since 1985. The global population increased by 197·2% (193·3–200·8) since 1950, from 2·6 billion (2·5–2·6) to 7·6 billion (7·4–7·9) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2·0%; this rate then remained nearly constant until 1970 and then decreased to 1·1% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2·5% in 1963 to 0·7% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2·7%. The global average age increased from 26·6 years in 1950 to 32·1 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59·9% to 65·3%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1·0 livebirths (95% UI 0·9–1·2) in Cyprus to a high of 7·1 livebirths (6·8–7·4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0·08 livebirths (0·07–0·09) in South Korea to 2·4 livebirths (2·2–2·6) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0·3 livebirths (0·3–0·4) in Puerto Rico to a high of 3·1 livebirths (3·0–3·2) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2·0% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2\ub75th percentile and 100 as the 97\ub75th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59\ub74 (IQR 35\ub74–67\ub73), ranging from a low of 11\ub76 (95% uncertainty interval 9\ub76–14\ub70) to a high of 84\ub79 (83\ub71–86\ub77). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of 'leaving no one behind', it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990-2017, projected indicators to 2030, and analysed global attainment. METHODS: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0-100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator

Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Methods: We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. Findings: Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3·9% (95% uncertainty interval [UI] 3·1-4·6) from 1990 to 2017; however, the all-age YLD rate increased by 7·2% (6·0-8·4) while the total sum of global YLDs increased from 562 million (421-723) to 853 million (642-1100). The increases for males and females were similar, with increases in all-age YLD rates of 7·9% (6·6-9·2) for males and 6·5% (5·4-7·7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782-3252] per 100 000 in males vs 1400 [1279-1524] per 100 000 in females), transport injuries (3322 [3082-3583] vs 2336 [2154-2535]), and self-harm and interpersonal violence (3265 [2943-3630] vs 5643 [5057-6302]). Interpretation: Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury

XLVIII Coloquio Argentino de Estadística. VI Jornada de Educación Estadística Martha Aliaga Modalidad virtual

Esta publicación es una compilación de las actividades realizadas en el marco del XLVIII Coloquio Argentino de Estadística y la VI Jornada de Educación Estadística Martha Aliaga organizada por la Sociedad Argentina de Estadística y la Facultad de Ciencias Económicas. Se presenta un resumen para cada uno de los talleres, cursos realizados, ponencias y poster presentados. Para los dos últimos se dispone de un hipervínculo que direcciona a la presentación del trabajo. Ellos obedecen a distintas temáticas de la estadística con una sesión especial destinada a la aplicación de modelos y análisis de datos sobre COVID-19.Fil: Saino, Martín. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Stimolo, María Inés. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ortiz, Pablo. Universidad Nacional de córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Guardiola, Mariana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Aguirre, Alberto Frank Lázaro. Universidade Federal de Alfenas. Departamento de Estatística. Instituto de Ciências Exatas; Brasil.Fil: Alves Nogueira, Denismar. Universidade Federal de Alfenas. Departamento de Estatística. Instituto de Ciências Exatas; Brasil.Fil: Beijo, Luiz Alberto. Universidade Federal de Alfenas. Departamento de Estatística. Instituto de Ciências Exatas; Brasil.Fil: Solis, Juan Manuel. Universidad Nacional de Jujuy. Centro de Estudios en Bioestadística, Bioinformática y Agromática; Argentina.Fil: Alabar, Fabio. Universidad Nacional de Jujuy. Centro de Estudios en Bioestadística, Bioinformática y Agromática; Argentina.Fil: Ruiz, Sebastián León. Universidad Nacional de Jujuy. Centro de Estudios en Bioestadística, Bioinformática y Agromática; Argentina.Fil: Hurtado, Rafael. Universidad Nacional de Jujuy; Argentina.Fil: Alegría Jiménez, Alfredo. Universidad Técnica Federico Santa María. Departamento de Matemática; Chile.Fil: Emery, Xavier. Universidad de Chile. Departamento de Ingeniería en Minas; Chile.Fil: Emery, Xavier. Universidad de Chile. Advanced Mining Technology Center; Chile.Fil: Álvarez-Vaz, Ramón. Universidad de la República. Instituto de Estadística. Departamento de Métodos Cuantitativos; Uruguay.Fil: Massa, Fernando. Universidad de la República. Instituto de Estadística. Departamento de Métodos Cuantitativos; Uruguay.Fil: Vernazza, Elena. Universidad de la República. Facultad de Ciencias Económicas y de Administración. Instituto de Estadística; Uruguay.Fil: Lezcano, Mikaela. Universidad de la República. Facultad de Ciencias Económicas y de Administración. Instituto de Estadística; Uruguay.Fil: Urruticoechea, Alar. Universidad Católica del Uruguay. Facultad de Ciencias de la Salud. Departamento de Neurocognición; Uruguay.Fil: del Callejo Canal, Diana. Universidad Veracruzana. Instituto de Investigación de Estudios Superiores, Económicos y Sociales; México.Fil: Canal Martínez, Margarita. Universidad Veracruzana. Instituto de Investigación de Estudios Superiores, Económicos y Sociales; México.Fil: Ruggia, Ornela. CONICET; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de desarrollo rural; Argentina.Fil: Tolosa, Leticia Eva. Universidad Nacional de Córdoba; Argentina. Universidad Católica de Córdoba; Argentina.Fil: Rojo, María Paula. Universidad Nacional de Córdoba; Argentina.Fil: Nicolas, María Claudia. Universidad Nacional de Córdoba; Argentina. Universidad Católica de Córdoba; Argentina.Fil: Barbaroy, Tomás. Universidad Nacional de Córdoba; Argentina.Fil: Villarreal, Fernanda. CONICET, Universidad Nacional del Sur. Instituto de Matemática de Bahía Blanca (INMABB); Argentina.Fil: Pisani, María Virginia. Universidad Nacional del Sur. Departamento de Matemática; Argentina.Fil: Quintana, Alicia. Universidad Nacional del Sur. Departamento de Matemática; Argentina.Fil: Elorza, María Eugenia. CONICET. Universidad Nacional del Sur. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina.Fil: Peretti, Gianluca. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Buzzi, Sergio Martín. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística y Matemática; Argentina.Fil: Settecase, Eugenia. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadísticas. Instituto de Investigaciones Teóricas y Aplicadas en Estadística; Argentina.Fil: Settecase, Eugenia. Department of Agriculture and Fisheries. Leslie Research Facility; Australia.Fil: Paccapelo, María Valeria. Department of Agriculture and Fisheries. Leslie Research Facility; Australia.Fil: Cuesta, Cristina. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadísticas. Instituto de Investigaciones Teóricas y Aplicadas en Estadística; Argentina.Fil: Saenz, José Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Luna, Silvia. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Paredes, Paula. Universidad Nacional de la Patagonia Austral; Argentina. Instituto Nacional de Tecnología Agropecuaria. Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Maglione, Dora. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Rosas, Juan E. Instituto Nacional de Investigación Agropecuaria (INIA); Uruguay.Fil: Pérez de Vida, Fernando. Instituto Nacional de Investigación Agropecuaria (INIA); Uruguay.Fil: Marella, Muzio. Sociedad Anónima Molinos Arroceros Nacionales (SAMAN); Uruguay.Fil: Berberian, Natalia. Universidad de la República. Facultad de Agronomía; Uruguay.Fil: Ponce, Daniela. Universidad Estadual Paulista. Facultad de Medicina; Brasil.Fil: Silveira, Liciana Vaz de A. Universidad Estadual Paulista; Brasil.Fil: Freitas Galletti, Agda Jessica de. Universidad Estadual Paulista; Brasil.Fil: Bellassai, Juan Carlos. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigación y Estudios de Matemáticas (CIEM-Conicet); Argentina.Fil: Pappaterra, María Lucía. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigación y Estudios de Matemáticas (CIEM-Conicet); Argentina.Fil: Ojeda, Silvia María. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina.Fil: Ascua, Melina Belén. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Roldán, Dana Agustina. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Rodi, Ayrton Luis. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Ventre, Giuliana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: González, Agustina. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas, Físico-Químicas y Naturales. Departamento de Matemática; Argentina.Fil: Palacio, Gabriela. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas, Físico-Químicas y Naturales. Departamento de Matemática; Argentina.Fil: Bigolin, Sabina. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas, Físico-Químicas y Naturales. Departamento de Matemática; Argentina.Fil: Ferrero, Susana. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas, Físico-Químicas y Naturales. Departamento de Matemática; Argentina.Fil: Del Medico, Ana Paula. Universidad Nacional de Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR); Argentina.Fil: Pratta, Guillermo. Universidad Nacional de Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR); Argentina.Fil: Tenaglia, Gerardo. Instituto Nacional de Tecnología Agropecuaria. Instituto de Investigación y Desarrollo Tecnológico para la Agricultura Familiar; Argentina.Fil: Lavalle, Andrea. Universidad Nacional del Comahue. Departamento de Estadística; Argentina.Fil: Demaio, Alejo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Hernández, Paz. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Di Palma, Fabricio. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Calizaya, Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Avalis, Francisca. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Caro, Norma Patricia. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Caro, Norma Patricia. Universidad Nacional de Córdoba. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Fernícola, Marcela. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina.Fil: Nuñez, Myriam. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina.Fil: Dundray, , Fabián. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina.Fil: Calviño, Amalia. Universidad de Buenos Aires. Instituto de Química y Metabolismo del Fármaco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Farfán Machaca, Yheni. Universidad Nacional de San Antonio Abad del Cusco. Departamento Académico de Matemáticas y Estadística; Argentina.Fil: Paucar, Guillermo. Universidad Nacional de San Antonio Abad del Cusco. Departamento Académico de Matemáticas y Estadística; Argentina.Fil: Coaquira, Frida. Universidad Nacional de San Antonio Abad del Cusco. Escuela de posgrado UNSAAC; Argentina.Fil: Ferreri, Noemí M. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina.Fil: Pascaner, Melina. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina.Fil: Martinez, Facundo. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina.Fil: Bossolasco, María Luisa. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo; Argentina.Fil: Bortolotto, Eugenia B. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina.Fil: Bortolotto, Eugenia B. Universidad Nacional de Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Faviere, Gabriela S. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina.Fil: Faviere, Gabriela S. Universidad Nacional de Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Angelini, Julia. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina.Fil: Angelini, Julia. Universidad Nacional de Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Cervigni, Gerardo. Universidad Nacional de Rosario. Centro de Estudios Fotosintéticos y Bioquímicos (CEFOBI); Argentina.Fil: Cervigni, Gerardo. Universidad Nacional de Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Valentini, Gabriel. Instituto Nacional de Tecnología Agropecuaria. Estación Experimental Agropecuaria INTA San Pedro; Argentina.Fil: Chiapella, Luciana C.. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas; Argentina.Fil: Chiapella, Luciana C. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); Argentina.Fil: Grendas, Leandro. Universidad Buenos Aires. Facultad de Medicina. Instituto de Farmacología; Argentina.Fil: Daray, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); Argentina.Fil: Daray, Federico. Universidad Buenos Aires. Facultad de Medicina. Instituto de Farmacología; Argentina.Fil: Leal, Danilo. Universidad Andrés Bello. Facultad de Ingeniería; Chile.Fil: Nicolis, Orietta. Universidad Andrés Bello. Facultad de Ingeniería; Chile.Fil: Bonadies, María Eugenia. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina.Fil: Ponteville, Christiane. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina.Fil: Catalano, Mara. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina.Fil: Catalano, Mara. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina.Fil: Dillon, Justina. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina.Fil: Carnevali, Graciela H. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina.Fil: Justo, Claudio Eduardo. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Agrimensura. Grupo de Aplicaciones Matemáticas y Estadísticas (UIDET); Argentina.Fil: Iglesias, Maximiliano. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Instituto de Estadística y Demografía; Argentina.Fil: Gómez, Pablo Sebastián. Universidad Nacional de Córdoba. Facultad de Ciencias Sociales. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Real, Ariel Hernán. Universidad Nacional de Luján. Departamento de Ciencias Básicas; Argentina.Fil: Vargas, Silvia Lorena. Universidad Nacional de Luján. Departamento de Ciencias Básicas; Argentina.Fil: López Calcagno, Yanil. Universidad Nacional de Luján. Departamento de Ciencias Básicas; Argentina.Fil: Batto, Mabel. Universidad Nacional de Luján. Departamento de Ciencias Básicas; Argentina.Fil: Sampaolesi, Edgardo. Universidad Nacional de Luján. Departamento de Ciencias Básicas; Argentina.Fil: Tealdi, Juan Manuel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Buzzi, Sergio Martín. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística y Matemática; Argentina.Fil: García Bazán, Gaspar. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Monroy Caicedo, Xiomara Alejandra. Universidad Nacional de Rosario; Argentina.Fil: Bermúdez Rubio, Dagoberto. Universidad Santo Tomás. Facultad de Estadística; Colombia.Fil: Ricci, Lila. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Centro Marplatense de Investigaciones Matemáticas; Argentina.Fil: Kelmansky, Diana Mabel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina.Fil: Rapelli, Cecilia. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas de la Escuela de Estadística; Argentina.Fil: García, María del Carmen. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas de la Escuela de Estadística; Argentina.Fil: Bussi, Javier. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística. Instituto de Investigaciones Teóricas y Aplicadas de la Escuela de Estadística; Argentina.Fil: Méndez, Fernanda. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística. Instituto de Investigaciones Teóricas y Aplicadas de la Escuela de Estadística (IITAE); Argentina.Fil: García Mata, Luis Ángel. Universidad Nacional Autónoma de México. Facultad de Estudios Superiores Acatlán; México.Fil: Ramírez González, Marco Antonio. Universidad Nacional Autónoma de México. Facultad de Estudios Superiores Acatlán; México.Fil: Rossi, Laura. Universidad Nacional de Cuyo. Facultad de Ciencias Económicas; Argentina.Fil: Vicente, Gonzalo. Universidad Nacional de Cuyo. Facultad de Ciencias Económicas; Argentina. Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas; España.Fil: Scavino, Marco. Universidad de la República. Facultad de Ciencias Económicas y de Administración. Instituto de Estadística; Uruguay.Fil: Estragó, Virginia. Presidencia de la República. Comisión Honoraria para la Salud Cardiovascular; Uruguay.Fil: Muñoz, Matías. Presidencia de la República. Comisión Honoraria para la Salud Cardiovascular; Uruguay.Fil: Castrillejo, Andrés. Universidad de la República. Facultad de Ciencias Económicas y de Administración. Instituto de Estadística; Uruguay.Fil: Da Rocha, Naila Camila. Universidade Estadual Paulista Júlio de Mesquita Filho- UNESP. Departamento de Bioestadística; BrasilFil: Macola Pacheco Barbosa, Abner. Universidade Estadual Paulista Júlio de Mesquita Filho- UNESP; Brasil.Fil: Corrente, José Eduardo. Universidade Estadual Paulista Júlio de Mesquita Filho – UNESP. Instituto de Biociencias. Departamento de Bioestadística; Brasil.Fil: Spataro, Javier. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Economía; Argentina.Fil: Salvatierra, Luca Mauricio. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Nahas, Estefanía. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Márquez, Viviana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Boggio, Gabriela. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística. Instituto de Investigaciones Teóricas y Aplicadas de la Escuela de Estadística; Argentina.Fil: Arnesi, Nora. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística. Instituto de Investigaciones Teóricas y Aplicadas de la Escuela de Estadística; Argentina.Fil: Harvey, Guillermina. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística. Instituto de Investigaciones Teóricas y Aplicadas de la Escuela de Estadística; Argentina.Fil: Settecase, Eugenia. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística. Instituto de Investigaciones Teóricas y Aplicadas de la Escuela de Estadística; Argentina.Fil: Wojdyla, Daniel. Duke University. Duke Clinical Research Institute; Estados Unidos.Fil: Blasco, Manuel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Instituto de Economía y Finanzas; Argentina.Fil: Stanecka, Nancy. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Instituto de Estadística y Demografía; Argentina.Fil: Caro, Valentina. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Instituto de Estadística y Demografía; Argentina.Fil: Sigal, Facundo. Universidad Austral. Facultad de Ciencias Empresariales. Departamento de Economía; Argentina.Fil: Blacona, María Teresa. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística. Escuela de Estadística; Argentina.Fil: Rodriguez, Norberto Vicente. Universidad Nacional de Tres de Febrero; Argentina.Fil: Loiacono, Karina Valeria. Universidad Nacional de Tres de Febrero; Argentina.Fil: García, Gregorio. Instituto Nacional de Estadística y Censos. Dirección Nacional de Metodología Estadística; Argentina.Fil: Ciardullo, Emanuel. Instituto Nacional de Estadística y Censos. Dirección Nacional de Metodología Estadística; Argentina.Fil: Ciardullo, Emanuel. Instituto Nacional de Estadística y Censos. Dirección Nacional de Metodología Estadística; Argentina.Fil: Funkner, Sofía. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Dieser, María Paula. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Martín, María Cristina. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Martín, María Cristina. Universidad Nacional del Sur. Departamento de Matemática; Argentina.Fil: Peitton, Lucas. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística; Argentina. Queensland Department of Agriculture and Fisheries; Australia.Fil: Borgognone, María Gabriela. Queensland Department of Agriculture and Fisheries; Australia.Fil: Terreno, Dante D. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Contabilidad; Argentina.Fil: Castro González, Enrique L. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Contabilidad; Argentina.Fil: Roldán, Janina Micaela. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: González, Gisela Paula. CONICET. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina. Universidad Nacional del Sur; Argentina.Fil: De Santis, Mariana. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Fil: Geri, Milva. CONICET. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina.Fil: Geri, Milva. Universidad Nacional del Sur. Departamento de Economía; Argentina. Universidad Nacional del Sur. Departamento de Matemática; Argentina.Fil: Marfia, Martín. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Ciencias Básicas; Argentina.Fil: Kudraszow, Nadia L. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Matemática de La Plata; Argentina.Fil: Closas, Humberto. Universidad Tecnológica Nacional; Argentina.Fil: Amarilla, Mariela. Universidad Tecnológica Nacional; Argentina.Fil: Jovanovich, Carina. Universidad Tecnológica Nacional; Argentina.Fil: de Castro, Idalia. Universidad Nacional del Nordeste; Argentina.Fil: Franchini, Noelia. Universidad Nacional del Nordeste; Argentina.Fil: Cruz, Rosa. Universidad Nacional del Nordeste; Argentina.Fil: Dusicka, Alicia. Universidad Nacional del Nordeste; Argentina.Fil: Quaglino, Marta. Universidad Nacional de Rosario; Argentina.Fil: Kalauz, Roberto José Andrés. Investigador Independiente; Argentina.Fil: González, Mariana Verónica. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística y Matemáticas; Argentina.Fil: Lescano, Maira Celeste.

Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015.

BACKGROUND: Healthy life expectancy (HALE) and disability-adjusted life-years (DALYs) provide summary measures of health across geographies and time that can inform assessments of epidemiological patterns and health system performance, help to prioritise investments in research and development, and monitor progress toward the Sustainable Development Goals (SDGs). We aimed to provide updated HALE and DALYs for geographies worldwide and evaluate how disease burden changes with development. METHODS: We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2015. We calculated DALYs by summing years of life lost (YLLs) and years of life lived with disability (YLDs) for each geography, age group, sex, and year. We estimated HALE using the Sullivan method, which draws from age-specific death rates and YLDs per capita. We then assessed how observed levels of DALYs and HALE differed from expected trends calculated with the Socio-demographic Index (SDI), a composite indicator constructed from measures of income per capita, average years of schooling, and total fertility rate. FINDINGS: Total global DALYs remained largely unchanged from 1990 to 2015, with decreases in communicable, neonatal, maternal, and nutritional (Group 1) disease DALYs offset by increased DALYs due to non-communicable diseases (NCDs). Much of this epidemiological transition was caused by changes in population growth and ageing, but it was accelerated by widespread improvements in SDI that also correlated strongly with the increasing importance of NCDs. Both total DALYs and age-standardised DALY rates due to most Group 1 causes significantly decreased by 2015, and although total burden climbed for the majority of NCDs, age-standardised DALY rates due to NCDs declined. Nonetheless, age-standardised DALY rates due to several high-burden NCDs (including osteoarthritis, drug use disorders, depression, diabetes, congenital birth defects, and skin, oral, and sense organ diseases) either increased or remained unchanged, leading to increases in their relative ranking in many geographies. From 2005 to 2015, HALE at birth increased by an average of 2·9 years (95% uncertainty interval 2·9-3·0) for men and 3·5 years (3·4-3·7) for women, while HALE at age 65 years improved by 0·85 years (0·78-0·92) and 1·2 years (1·1-1·3), respectively. Rising SDI was associated with consistently higher HALE and a somewhat smaller proportion of life spent with functional health loss; however, rising SDI was related to increases in total disability. Many countries and territories in central America and eastern sub-Saharan Africa had increasingly lower rates of disease burden than expected given their SDI. At the same time, a subset of geographies recorded a growing gap between observed and expected levels of DALYs, a trend driven mainly by rising burden due to war, interpersonal violence, and various NCDs. INTERPRETATION: Health is improving globally, but this means more populations are spending more time with functional health loss, an absolute expansion of morbidity. The proportion of life spent in ill health decreases somewhat with increasing SDI, a relative compression of morbidity, which supports continued efforts to elevate personal income, improve education, and limit fertility. Our analysis of DALYs and HALE and their relationship to SDI represents a robust framework on which to benchmark geography-specific health performance and SDG progress. Country-specific drivers of disease burden, particularly for causes with higher-than-expected DALYs, should inform financial and research investments, prevention efforts, health policies, and health system improvement initiatives for all countries along the development continuum. FUNDING: Bill & Melinda Gates Foundation

Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990-2015 : a novel analysis from the Global Burden of Disease Study 2015

Background National levels of personal health-care access and quality can be approximated by measuring mortality rates from causes that should not be fatal in the presence of effective medical care (ie, amenable mortality). Previous analyses of mortality amenable to health care only focused on high-income countries and faced several methodological challenges. In the present analysis, we use the highly standardised cause of death and risk factor estimates generated through the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to improve and expand the quantification of personal health-care access and quality for 195 countries and territories from 1990 to 2015. Methods We mapped the most widely used list of causes amenable to personal health care developed by Nolte and McKee to 32 GBD causes. We accounted for variations in cause of death certification and misclassifications through the extensive data standardisation processes and redistribution algorithms developed for GBD. To isolate the effects of personal health-care access and quality, we risk-standardised cause-specific mortality rates for each geography-year by removing the joint effects of local environmental and behavioural risks, and adding back the global levels of risk exposure as estimated for GBD 2015. We employed principal component analysis to create a single, interpretable summary measure-the Healthcare Quality and Access (HAQ) Index-on a scale of 0 to 100. The HAQ Index showed strong convergence validity as compared with other health-system indicators, including health expenditure per capita (r= 0.88), an index of 11 universal health coverage interventions (r= 0.83), and human resources for health per 1000 (r= 0.77). We used free disposal hull analysis with bootstrapping to produce a frontier based on the relationship between the HAQ Index and the Socio-demographic Index (SDI), a measure of overall development consisting of income per capita, average years of education, and total fertility rates. This frontier allowed us to better quantify the maximum levels of personal health-care access and quality achieved across the development spectrum, and pinpoint geographies where gaps between observed and potential levels have narrowed or widened over time. Findings Between 1990 and 2015, nearly all countries and territories saw their HAQ Index values improve; nonetheless, the difference between the highest and lowest observed HAQ Index was larger in 2015 than in 1990, ranging from 28.6 to 94.6. Of 195 geographies, 167 had statistically significant increases in HAQ Index levels since 1990, with South Korea, Turkey, Peru, China, and the Maldives recording among the largest gains by 2015. Performance on the HAQ Index and individual causes showed distinct patterns by region and level of development, yet substantial heterogeneities emerged for several causes, including cancers in highest-SDI countries; chronic kidney disease, diabetes, diarrhoeal diseases, and lower respiratory infections among middle-SDI countries; and measles and tetanus among lowest-SDI countries. While the global HAQ Index average rose from 40.7 (95% uncertainty interval, 39.0-42.8) in 1990 to 53.7 (52.2-55.4) in 2015, far less progress occurred in narrowing the gap between observed HAQ Index values and maximum levels achieved; at the global level, the difference between the observed and frontier HAQ Index only decreased from 21.2 in 1990 to 20.1 in 2015. If every country and territory had achieved the highest observed HAQ Index by their corresponding level of SDI, the global average would have been 73.8 in 2015. Several countries, particularly in eastern and western sub-Saharan Africa, reached HAQ Index values similar to or beyond their development levels, whereas others, namely in southern sub-Saharan Africa, the Middle East, and south Asia, lagged behind what geographies of similar development attained between 1990 and 2015. Interpretation This novel extension of the GBD Study shows the untapped potential for personal health-care access and quality improvement across the development spectrum. Amid substantive advances in personal health care at the national level, heterogeneous patterns for individual causes in given countries or territories suggest that few places have consistently achieved optimal health-care access and quality across health-system functions and therapeutic areas. This is especially evident in middle-SDI countries, many of which have recently undergone or are currently experiencing epidemiological transitions. The HAQ Index, if paired with other measures of health-systemcharacteristics such as intervention coverage, could provide a robust avenue for tracking progress on universal health coverage and identifying local priorities for strengthening personal health-care quality and access throughout the world. Copyright (C) The Author(s). Published by Elsevier Ltd.Peer reviewe

Measuring the health-related Sustainable Development Goals in 188 countries : a baseline analysis from the Global Burden of Disease Study 2015

Background In September, 2015, the UN General Assembly established the Sustainable Development Goals (SDGs). The SDGs specify 17 universal goals, 169 targets, and 230 indicators leading up to 2030. We provide an analysis of 33 health-related SDG indicators based on the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015). Methods We applied statistical methods to systematically compiled data to estimate the performance of 33 health-related SDG indicators for 188 countries from 1990 to 2015. We rescaled each indicator on a scale from 0 (worst observed value between 1990 and 2015) to 100 (best observed). Indices representing all 33 health-related SDG indicators (health-related SDG index), health-related SDG indicators included in the Millennium Development Goals (MDG index), and health-related indicators not included in the MDGs (non-MDG index) were computed as the geometric mean of the rescaled indicators by SDG target. We used spline regressions to examine the relations between the Socio-demographic Index (SDI, a summary measure based on average income per person, educational attainment, and total fertility rate) and each of the health-related SDG indicators and indices. Findings In 2015, the median health-related SDG index was 59.3 (95% uncertainty interval 56.8-61.8) and varied widely by country, ranging from 85.5 (84.2-86.5) in Iceland to 20.4 (15.4-24.9) in Central African Republic. SDI was a good predictor of the health-related SDG index (r(2) = 0.88) and the MDG index (r(2) = 0.2), whereas the non-MDG index had a weaker relation with SDI (r(2) = 0.79). Between 2000 and 2015, the health-related SDG index improved by a median of 7.9 (IQR 5.0-10.4), and gains on the MDG index (a median change of 10.0 [6.7-13.1]) exceeded that of the non-MDG index (a median change of 5.5 [2.1-8.9]). Since 2000, pronounced progress occurred for indicators such as met need with modern contraception, under-5 mortality, and neonatal mortality, as well as the indicator for universal health coverage tracer interventions. Moderate improvements were found for indicators such as HIV and tuberculosis incidence, minimal changes for hepatitis B incidence took place, and childhood overweight considerably worsened. Interpretation GBD provides an independent, comparable avenue for monitoring progress towards the health-related SDGs. Our analysis not only highlights the importance of income, education, and fertility as drivers of health improvement but also emphasises that investments in these areas alone will not be sufficient. Although considerable progress on the health-related MDG indicators has been made, these gains will need to be sustained and, in many cases, accelerated to achieve the ambitious SDG targets. The minimal improvement in or worsening of health-related indicators beyond the MDGs highlight the need for additional resources to effectively address the expanded scope of the health-related SDGs.Peer reviewe