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

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Study Justification:
The study aims to provide updated information on healthy life expectancy (HALE) and disability-adjusted life-years (DALYs) for different regions and countries worldwide. This information can help assess epidemiological patterns, evaluate health system performance, prioritize research and development investments, and monitor progress towards the Sustainable Development Goals (SDGs).
Highlights:
– Total global DALYs remained largely unchanged from 1990 to 2015, with decreases in communicable, neonatal, maternal, and nutritional diseases offset by increased DALYs due to non-communicable diseases (NCDs).
– The epidemiological transition towards NCDs was accelerated by improvements in socio-demographic indicators, such as income per capita, average years of schooling, and total fertility rate.
– Age-standardized DALY rates due to most Group 1 causes (communicable, neonatal, maternal, and nutritional diseases) significantly decreased by 2015, while age-standardized DALY rates due to NCDs declined.
– However, age-standardized DALY rates due to certain 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.
– HALE at birth increased by an average of 2.9 years for men and 3.5 years for women from 2005 to 2015. HALE at age 65 also improved.
– Rising socio-demographic index (SDI) was associated with higher HALE and a smaller proportion of life spent with functional health loss, but it was also related to increases in total disability.
– Some countries and territories had lower disease burden rates than expected given their SDI, while others recorded a growing gap between observed and expected levels of DALYs, driven by war, interpersonal violence, and various NCDs.
Recommendations:
– Efforts should continue to elevate personal income, improve education, and limit fertility to reduce the proportion of life spent in ill health.
– Country-specific drivers of disease burden, especially for causes with higher-than-expected DALYs, should inform financial and research investments, prevention efforts, health policies, and health system improvement initiatives.
– Monitoring and benchmarking geography-specific health performance and SDG progress using DALYs and HALE can help guide policy decisions and resource allocation.
Key Role Players:
– Researchers and analysts to collect and analyze data on disease burden, mortality, and non-fatal health loss.
– Health policymakers and government officials to implement evidence-based interventions and policies.
– Healthcare providers and professionals to deliver appropriate healthcare services.
– Non-governmental organizations and community-based organizations to support prevention efforts and health promotion.
– Funding agencies and donors to provide financial support for research, interventions, and health system improvements.
Cost Items for Planning Recommendations:
– Data collection and analysis: funding for research studies, surveys, and data collection tools.
– Intervention implementation: funding for healthcare services, prevention programs, and health promotion campaigns.
– Health system improvement: funding for infrastructure development, training programs, and capacity building.
– Monitoring and evaluation: funding for surveillance systems, data management, and reporting mechanisms.
– Research and development: funding for innovative solutions, new treatments, and technologies.
– Policy development and implementation: funding for policy formulation, stakeholder engagement, and policy advocacy.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on the Global Burden of Disease Study 2015 and provides updated HALE and DALYs for 195 countries and territories. The study used robust methods and data sources to estimate mortality and non-fatal health loss. However, to improve the evidence, it would be helpful to provide more details on the specific methods used for estimating DALYs and HALE, as well as the limitations of the study.

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.

Detailed methods for estimating DALYs and HALE, including analytic approaches for mortality and non-fatal health loss estimation, are provided in related publications.12, 20 Additional detail on GBD metrics and definitions are found elsewhere.21 Interactive tools are also available to explore GBD 2015 results and data sources. This analysis follows the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER), which includes recommendations on documentation of data sources, estimation methods, and statistical analysis.22, 23 In brief, the GBD geographic hierarchy involves 519 total geographies within 195 countries and territories, 21 regions, and seven super-regions. This study reports results for all countries and territories. The GBD cause hierarchy has four levels of classification and causes reported within each level that are mutually exclusive and collectively exhaustive. The full GBD cause list with corresponding International Classification of Diseases (ICD)-9 and ICD-10 codes are available in our publications on cause-specific mortality12 and non-fatal health outcomes.20 We estimated all-cause and cause-specific mortality with a multistep computation process, which included systematically addressing known data challenges, such as different coding schemes, different age-group reporting, variation in certification, misclassification of HIV/AIDS deaths in some countries, misclassification of maternal HIV/AIDS deaths, and incorporation of population-based cancer registry data, before computation of cause-specific mortality with analytic tools such as Cause of Death Ensemble Modelling (CODEm). Each death could have only one underlying cause. Additional detail, including model specifications and data availability for each cause-specific model, can be found in the supplementary material of the GBD 2015 mortality and causes of death publication.12 We calculated normative life tables based on the lowest death rates for each age group among geographies with total populations greater than 5 million. We computed cause-specific YLLs by multiplying cause-specific deaths by the life expectancy at the age of death (ie, 86·59 years at age 0 years; 23·79 years at age 65 years) from this normative life table, and then used the GBD world population age standard to calculate age-standardised mortality rates and YLL rates.12 Our most commonly used analytic approach to estimate non-fatal health loss was DisMod-MR 2.1, a Bayesian meta-regression tool that synthesises diverse data sources to produce internally consistent estimates of incidence, prevalence, remission, and excess mortality. The use of other methods to estimate non-fatal health loss was determined by cause-specific data availability and epidemiological characteristics.24 Additional detail, including model specifications and data availability for each cause-specific model, can be found in the supplementary material of the GBD 2015 non-fatal publication.20 Each non-fatal sequela was estimated separately. We then applied a microsimulation framework to assess the occurrence of comorbidity in each age group, sex, geography, and year separately. Disability from comorbid conditions was apportioned to each of the contributing causes. GBD disability weights were based on population surveys with more than 60 000 respondents, and previous studies show that disability weights do not significantly vary across geographies, income, or educational attainment.25, 26 In this study, disability weights are invariant over geography and time, although the distribution of sequelae, and therefore the severity and cumulative disability per case of a condition, can differ by age, sex, geography, and year. DALYs are the sum of YLLs and YLDs as estimated in GBD 2015 for each cause, geography, age group, sex, and year.12, 20 Using methods developed by Sullivan,7 we calculated HALE by age group within abridged multiple-decrement life tables and estimates of YLDs per capita for each geography–age–sex–year from 1990 to 2015.8, 10, 27 For all results, we report 95% uncertainty intervals (UIs), which were derived from 1000 draws from the posterior distribution of each step in the estimation process. UIs are distinct from confidence intervals, because confidence intervals only capture the uncertainty associated with sampling error, whereas uncertainty intervals provide a method for propagation of uncertainty from multiple sources including sampling, model estimation, and model specification. 95% UIs represent the ordinal 25th and 975th draw of the quantity of interest. For mortality and YLLs, UIs reflect uncertainty that arises from sample sizes of studies used as data sources, adjustments to sources of all-cause mortality, parameter uncertainty in model estimation, and specification uncertainty for all-cause and cause-specific models. For prevalence, incidence, and YLDs, UIs reflect the uncertainty that arises from sample sizes of studies used as data sources, data adjustments from non-reference definitions, parameter uncertainty in model estimation, and uncertainty in the disability weights. In the absence of any direction information about the correlation between uncertainty in YLLs and YLDs, we assumed uncertainty in age-specific YLDs is independent of age-specific YLLs in DALYs and death rates in HALE. We examined the relationship between DALYs, HALE, and the Socio-demographic Index (SDI).28 SDI was constructed based on the geometric mean of three indicators: income per capita, average years of schooling among people aged 15 years or older, and the total fertility rate. SDI values were scaled to a range of 0 to 1, with 0 equalling the lowest income, lowest schooling, and highest fertility rate observed from 1980 to 2015, and 1 equalling the highest income, highest schooling, and lowest fertility rate assessed during that time. The average relationships between each summary health measure and SDI were estimated using spline regressions. These regressions were used to estimate expected values at each level of SDI. Additional detail on SDI computation and geography-specific SDI values are available in the appendix (pp 4–5 and pp 74–80). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

The provided text is the title and description of a research study on global health metrics. It does not provide specific innovations or recommendations for improving access to maternal health. To improve access to maternal health, some potential innovations and recommendations could include:

1. Telemedicine: Using technology to provide remote consultations and support for pregnant women, allowing them to access healthcare services without having to travel long distances.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources for pregnant women, such as prenatal care guidelines, appointment reminders, and educational materials.

3. Community health workers: Training and deploying community health workers to provide basic prenatal care, education, and support to pregnant women in underserved areas.

4. Transportation solutions: Implementing transportation initiatives, such as ambulance services or transportation vouchers, to ensure that pregnant women can easily access healthcare facilities for prenatal care and delivery.

5. Maternal waiting homes: Establishing safe and comfortable accommodations near healthcare facilities where pregnant women from remote areas can stay during the final weeks of pregnancy, ensuring they are close to medical care when needed.

6. Financial incentives: Providing financial incentives, such as cash transfers or insurance coverage, to encourage pregnant women to seek prenatal care and deliver in healthcare facilities.

7. Maternal health education: Implementing comprehensive maternal health education programs in communities to raise awareness about the importance of prenatal care, nutrition, and safe delivery practices.

8. Task-shifting: Training and empowering non-physician healthcare providers, such as nurses and midwives, to provide essential maternal health services, thereby increasing the availability of skilled care.

9. Quality improvement initiatives: Implementing quality improvement programs in healthcare facilities to ensure that maternal health services are provided in a safe and effective manner.

10. Policy and advocacy: Advocating for policies and funding that prioritize maternal health and address the underlying social, economic, and cultural factors that contribute to disparities in access to care.

It is important to note that these recommendations should be tailored to the specific context and needs of each community or region.
AI Innovations Description
The provided text is a detailed description of a study on global, regional, and national disability-adjusted life-years (DALYs) for various diseases and injuries, as well as healthy life expectancy (HALE), from 1990 to 2015. The study aimed to assess epidemiological patterns, health system performance, and progress towards the Sustainable Development Goals (SDGs). It used data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) to calculate DALYs and HALE for different geographies and evaluate changes in disease burden with development.

While the information provided is valuable for understanding global health trends, it does not directly address the request for recommendations to improve access to maternal health. To develop innovations that can improve access to maternal health, it is important to consider specific challenges and barriers faced by pregnant women and mothers in accessing healthcare services. These may include geographical barriers, lack of infrastructure, limited availability of skilled healthcare providers, cultural and social factors, and financial constraints.

Based on these considerations, here are some general recommendations that can be used to develop innovations to improve access to maternal health:

1. Telemedicine and mobile health technologies: Develop and implement telemedicine and mobile health solutions to provide remote consultations, prenatal care, and postnatal support to pregnant women in remote or underserved areas.

2. Community-based healthcare models: Establish community-based healthcare models that bring essential maternal health services closer to women, including antenatal care, skilled birth attendance, and postnatal care. This can involve training and deploying community health workers or midwives to provide care in local communities.

3. Transportation and referral systems: Improve transportation infrastructure and establish effective referral systems to ensure that pregnant women can access healthcare facilities in a timely manner, especially in rural or remote areas.

4. Financial support and health insurance: Implement policies and programs that provide financial support for maternal healthcare, such as subsidies for prenatal and delivery services, and ensure that pregnant women have access to affordable health insurance coverage.

5. Health education and awareness: Conduct health education campaigns to raise awareness about the importance of maternal health, promote early antenatal care, and educate women and their families about pregnancy-related complications and warning signs.

6. Strengthening healthcare facilities: Invest in improving the quality and capacity of healthcare facilities, including maternity wards, by providing necessary equipment, supplies, and training for healthcare providers.

7. Partnerships and collaborations: Foster partnerships and collaborations between governments, non-governmental organizations, healthcare providers, and communities to collectively address the challenges and barriers to maternal health access.

It is important to note that these recommendations should be tailored to the specific context and needs of each community or region.
AI Innovations Methodology
The provided text is a detailed description of the methodology used in a study on disability-adjusted life-years (DALYs) and healthy life expectancy (HALE) for various diseases and injuries. It does not specifically address innovations to improve access to maternal health or provide a methodology to simulate the impact of recommendations on improving access to maternal health.

However, I can provide you with some potential innovations and a general methodology to simulate the impact of these recommendations on improving access to maternal health:

1. Telemedicine: Implementing telemedicine services can improve access to maternal health by allowing pregnant women in remote or underserved areas to consult with healthcare professionals remotely. This can help in early detection of complications and provide necessary guidance and support.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information on prenatal care, nutrition, and maternal health can empower pregnant women to take better care of themselves. These apps can also provide reminders for prenatal visits and medication schedules.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, education, and support to pregnant women in their communities can improve access to maternal health services, especially in rural areas.

4. Transportation solutions: Addressing transportation barriers by providing affordable and accessible transportation options for pregnant women can ensure they can reach healthcare facilities in a timely manner.

Methodology to simulate the impact of these recommendations:

1. Define the target population: Identify the specific population group that will benefit from the innovations, such as pregnant women in a particular region or community.

2. Collect baseline data: Gather data on the current access to maternal health services, including the number of prenatal visits, maternal mortality rates, and distance to healthcare facilities.

3. Define indicators: Determine the key indicators that will be used to measure the impact of the recommendations, such as the increase in the number of prenatal visits or the reduction in maternal mortality rates.

4. Simulate the impact: Use modeling techniques to simulate the potential impact of the recommendations on the defined indicators. This can involve creating scenarios with different levels of implementation and estimating the resulting changes in the indicators.

5. Validate the simulation: Validate the simulation results by comparing them with real-world data, if available. This can help assess the accuracy and reliability of the simulation.

6. Refine and iterate: Based on the simulation results and validation, refine the recommendations and simulation methodology as needed. Iterate the process to further improve the accuracy of the simulations and assess the potential impact of different implementation strategies.

It is important to note that the specific methodology and data requirements may vary depending on the context and available resources. Consulting with experts in the field and utilizing existing research and data sources can further enhance the accuracy and reliability of the simulations.

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