Health in times of uncertainty in the eastern Mediterranean region, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

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Study Justification:
The study titled “Health in times of uncertainty in the eastern Mediterranean region, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013” aims to present the burden of diseases, injuries, and risk factors in the eastern Mediterranean region as of 2013. This is important because the region has faced unrest, revolutions, wars, and the so-called Arab uprisings, which have had a significant impact on health and resources. The study provides valuable insights into the health challenges faced by the region and the need for increased investment in health and resolution of conflicts.
Highlights:
– Ischaemic heart disease was the leading cause of death in the region in 2013, with an increase of 17.2% since 1990.
– Diarrhoeal diseases were the leading cause of death in Somalia in 2013, but the rate decreased by 26.9% since 1990.
– Ischaemic heart disease and lower respiratory infection were the leading causes of disability-adjusted life-years (DALYs) for males and females, respectively.
– High blood pressure was the leading risk factor for DALYs in 2013, with an increase of 83.3% since 1990.
– Risk factors for DALYs varied by country, with non-communicable risk factors being the leading cause in high-income and middle-income countries.
– The crises in Egypt, Yemen, Libya, and Syria have resulted in a reduction in life expectancy in those countries.
– The study highlights the impact of population growth, ageing, and ongoing conflicts on health outcomes in the region.
Recommendations:
Based on the findings of the study, the following recommendations are made:
1. Increased investment in health in the eastern Mediterranean region.
2. Resolution of ongoing conflicts in the region.
3. Focus on addressing non-communicable risk factors in high-income and middle-income countries.
4. Targeted interventions to address specific health challenges, such as childhood wasting in low-income countries and unsafe sex in Djibouti.
5. Monitoring and addressing the burden of mental health problems and drug use.
6. Consideration of the impact of population growth and ageing on health planning.
Key Role Players:
To address the recommendations, the following key role players are needed:
1. Government health ministries and departments in the eastern Mediterranean countries.
2. International organizations, such as the World Health Organization (WHO) and United Nations agencies.
3. Non-governmental organizations (NGOs) working in the region.
4. Health professionals and researchers specializing in the eastern Mediterranean region.
5. Community leaders and local stakeholders.
Cost Items for Planning Recommendations:
While the actual cost of implementing the recommendations is not provided, the following cost items should be considered in planning:
1. Healthcare infrastructure development and improvement.
2. Health workforce training and capacity building.
3. Procurement of medical equipment and supplies.
4. Implementation of health promotion and prevention programs.
5. Research and data collection on health indicators and outcomes.
6. Monitoring and evaluation of health interventions.
7. Public awareness campaigns and community engagement initiatives.
8. Collaboration and coordination between different stakeholders.
Please note that the actual cost will depend on the specific interventions and strategies chosen to address the recommendations.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on the Global Burden of Disease Study 2013, which includes an annual assessment covering 188 countries from 1990 to 2013. The study covers a wide range of diseases, injuries, and risk factors. The authors also mention key methodological differences from the previous Global Burden of Disease Study 2010, which include the inclusion of new data through updated systematic reviews and the contribution of unpublished data sources. To improve the evidence, the authors could provide more specific details about the methodology used in the study and the sources of data.

Background The eastern Mediterranean region is comprised of 22 countries: Afghanistan, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, the United Arab Emirates, and Yemen. Since our Global Burden of Disease Study 2010 (GBD 2010), the region has faced unrest as a result of revolutions, wars, and the so-called Arab uprisings. The objective of this study was to present the burden of diseases, injuries, and risk factors in the eastern Mediterranean region as of 2013. Methods GBD 2013 includes an annual assessment covering 188 countries from 1990 to 2013. The study covers 306 diseases and injuries, 1233 sequelae, and 79 risk factors. Our GBD 2013 analyses included the addition of new data through updated systematic reviews and through the contribution of unpublished data sources from collaborators, an updated version of modelling software, and several improvements in our methods. In this systematic analysis, we use data from GBD 2013 to analyse the burden of disease and injuries in the eastern Mediterranean region specifically. Findings The leading cause of death in the region in 2013 was ischaemic heart disease (90·3 deaths per 100 000 people), which increased by 17·2% since 1990. However, diarrhoeal diseases were the leading cause of death in Somalia (186·7 deaths per 100 000 people) in 2013, which decreased by 26·9% since 1990. The leading cause of disability-adjusted life-years (DALYs) was ischaemic heart disease for males and lower respiratory infection for females. High blood pressure was the leading risk factor for DALYs in 2013, with an increase of 83·3% since 1990. Risk factors for DALYs varied by country. In low-income countries, childhood wasting was the leading cause of DALYs in Afghanistan, Somalia, and Yemen, whereas unsafe sex was the leading cause in Djibouti. Non-communicable risk factors were the leading cause of DALYs in high-income and middle-income countries in the region. DALY risk factors varied by age, with child and maternal malnutrition affecting the younger age groups (aged 28 days to 4 years), whereas high bodyweight and systolic blood pressure affected older people (aged 60–80 years). The proportion of DALYs attributed to high body-mass index increased from 3·7% to 7·5% between 1990 and 2013. Burden of mental health problems and drug use increased. Most increases in DALYs, especially from non-communicable diseases, were due to population growth. The crises in Egypt, Yemen, Libya, and Syria have resulted in a reduction in life expectancy; life expectancy in Syria would have been 5 years higher than that recorded for females and 6 years higher for males had the crisis not occurred. Interpretation Our study shows that the eastern Mediterranean region is going through a crucial health phase. The Arab uprisings and the wars that followed, coupled with ageing and population growth, will have a major impact on the region’s health and resources. The region has historically seen improvements in life expectancy and other health indicators, even under stress. However, the current situation will cause deteriorating health conditions for many countries and for many years and will have an impact on the region and the rest of the world. Based on our findings, we call for increased investment in health in the region in addition to reducing the conflicts. Funding Bill & Melinda Gates Foundation.

The Global Burden of Disease Study 2013 (GBD 2013) includes an annual assessment covering 188 countries from 1990 to 2013. It covers 306 diseases and injuries, 1233 sequelae, and 79 risk factors. Detailed descriptions of the method and approach of GBD 2013 have been published elsewhere.4, 5, 6, 7 Key changes in the methods from GBD 2010 are the inclusion of new data through updated systematic reviews and the contribution of unpublished data sources from various collaborators; the use of a counterfactual approach for estimating causes of diarrhoea and pneumonia; elaboration of the sequelae list to include asymptomatic states; use of more detailed nature of injury codes (N-codes); improvements to the Bayesian meta-regression method; increased simulation size for comorbidity; estimation of the prevalence of injuries by cohort; and use of a new method to estimate the distribution of mild, moderate, and severe anaemia by cause. In this systematic analysis, we use data from GBD 2013 to analyse the burden of disease and injuries in the eastern Mediterranean region specifically. We report 95% uncertainty intervals for each value in our analysis. Evidence before this study The Global Burden of Disease Study 2010 (GBD 2010) became available in 2012. GBD 2010 reported on disability-adjusted life-years (DALYs), health-adjusted life expectancy, and 67 risks and risk clusters by 21 world regions and 188 countries. GBD 2010 covered 20 age and sex groups. Added value of this study GBD 2013 includes an annual assessment covering 188 countries, from 1990 to 2013. It covers 306 diseases and injuries, 1233 sequelae, and 79 risk factors. GBD 2013 included key methodological differences from GBD 2010, which were inclusion of new data through updated systematic reviews and through the contribution of unpublished data sources from many collaborators; use of a counterfactual approach for estimating diarrhoea and pneumonia causes; elaboration of the sequelae list to include asymptomatic states; use of more detailed nature of injury codes (N-codes); improvements to the Bayesian meta-regression method; increased simulation size for comorbidity; estimation of the prevalence of injuries by cohort; and use of a new method to estimate the distribution of mild, moderate, and severe anaemia by cause. This study provides an overview of the comprehensive burden of diseases and risk factors for the eastern Mediterranean region. Implications of all the available evidence The eastern Mediterranean region is facing numerous health challenges, as a result of previous wars, revolutions, the wars that followed, and ageing and population growth. These challenges will have a major impact on health outcomes and available resources. The region has historically seen improvements in life expectancy and other health indicators even under stress. However, according to our study, the current situation has resulted in deteriorating health conditions for many countries that are threatening these gains and will have an impact on the region and the rest of the world. On the basis of our data, we call for increased investment in health in the region and the end of ongoing conflicts. We generated child mortality rates and adult mortality rates under the influence of natural disasters and armed conflicts as previously described.7 Because of ongoing unrest and war, some datapoints needed for our analyses were not available. A list of all datapoints used in this study are available on our Global Health Data Exchange web page. When data were unavailable, we relied on our ensemble modelling techniques to generate the estimates using other available variables and the information for neighbouring countries or countries with a similar health profile in the region. We used six different modelling strategies for the 240 causes of death using our cause-of-death ensemble model for causes with sufficient information. We estimated national time series from 1950 to 2013 for gross domestic product, educational attainment, tobacco prevalence, and obesity. For mortality rates of children younger than 5 years, we analysed all survey, census, sample registration, and vital registration sources. Wherever possible, we analysed microdata from surveys and censuses with updated methods for child mortality. We corrected for bias in different sources in specific countries. For adult mortality, we identified all available vital registration data, sibling history survey data, sample registration data, and household recall of deaths. We assessed vital registration data for completeness and analysed sibling history data to account for survivor bias, zero-surviving sibships, and recall bias. We used spatiotemporal regression and Gaussian process regression to synthesise all measurements of mortality. We used UN population estimates that account for migration in our analyses. Moreover, we applied mortality shocks based on multiple reports to our analyses. We used the comparative risk assessment approach to evaluate how much of the burden of disease observed in a given year can be attributed to past exposure to a risk factor.7 We estimated attributable burden by comparing observed health outcomes with outcomes that would have been observed if an alternative or counterfactual level of exposure had taken place in the past. We used the exposure level that minimises risk for the population, termed the theoretical minimum risk exposure level. We avoided double counting in the presentation of overall results by computing the overlap for joint risk distributions: behavioural risks alone; environmental or occupational risks alone; metabolic risks alone; behavioural and environmental or occupational risks together; behavioural and metabolic risks together; environmental or occupational and metabolic risks together; and behavioural, environmental or occupational, and metabolic risks together. We cross-tabulated the quantiles of disability-adjusted life-years (DALYs) by quintiles of annual DALY increase from 1990 to 2013 to show rates of DALY increase by burden. We divided the region into three categories according to the gross national income (GNI) per capita. The first category represents the low-income countries with an average GNI per capita of US$523·3 On the opposite end of the spectrum are oil-rich, high-income countries with an average GNI per capita of $39 688. The nations that lie in between are the middle-income countries with an average GNI per capita of $3251, which can be further subdivided into lower-middle-income and upper-middle-income countries. Low-income countries were Afghanistan, Djibouti, Somalia, and Yemen. Middle-income countries were Egypt, Iran, Iraq, Jordan, Lebanon, Libya, Morocco, Pakistan, Palestine, Sudan, Syria, and Tunisia. High-income countries were Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. 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 to submit the paper.

Based on the provided information, it is not clear what specific innovations or recommendations are being sought to improve access to maternal health. However, here are some potential innovations that could be considered:

1. Telemedicine and mobile health: Utilizing technology to provide remote access to healthcare services, including prenatal care, consultations, and monitoring.

2. Community health workers: Training and deploying community health workers to provide maternal health education, support, and basic healthcare services in underserved areas.

3. Maternal health clinics: Establishing dedicated clinics or centers that focus specifically on maternal health, providing comprehensive care and specialized services.

4. Maternal health vouchers: Implementing voucher programs that provide financial assistance to pregnant women, enabling them to access quality maternal healthcare services.

5. Transportation support: Developing transportation systems or programs to ensure that pregnant women have access to transportation for prenatal visits, delivery, and postnatal care.

6. Maternal health education campaigns: Conducting targeted education campaigns to raise awareness about the importance of maternal health and promote healthy behaviors during pregnancy.

7. Maternal health monitoring systems: Implementing systems to track and monitor maternal health indicators, enabling early detection of complications and timely intervention.

8. Maternal health task forces: Establishing task forces or committees dedicated to improving maternal health, bringing together stakeholders from various sectors to develop and implement strategies.

These are just a few examples of potential innovations that could be considered to improve access to maternal health. The specific context and needs of the region should be taken into account when developing and implementing these innovations.
AI Innovations Description
Based on the information provided, it is not clear how the recommendation to improve access to maternal health can be derived from the given study on the Global Burden of Disease in the eastern Mediterranean region. The study focuses on analyzing the burden of diseases, injuries, and risk factors in the region, rather than specifically addressing maternal health. To develop a recommendation for improving access to maternal health, it would be necessary to review studies and evidence specifically related to maternal health in the region.
AI Innovations Methodology
To improve access to maternal health, here are some potential recommendations:

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals for prenatal and postnatal care. This can help overcome geographical barriers and increase access to maternal health services.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources on maternal health can empower women with knowledge and support. These apps can provide guidance on pregnancy, childbirth, breastfeeding, and postpartum care.

3. Community health workers: Training and deploying community health workers can improve access to maternal health services, especially in rural and underserved areas. These workers can provide education, counseling, and basic healthcare services to pregnant women and new mothers.

4. Transportation support: Providing transportation support, such as vouchers or subsidies, can help pregnant women reach healthcare facilities for prenatal check-ups, delivery, and postnatal care. This can be particularly beneficial in areas with limited transportation infrastructure.

5. Maternal waiting homes: Establishing maternal waiting homes near healthcare facilities can accommodate pregnant women who live far away. These homes provide a safe and comfortable place for women to stay before and after delivery, ensuring timely access to care.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Data collection: Gather data on the current state of maternal health access, including factors such as distance to healthcare facilities, availability of healthcare providers, and utilization rates.

2. Define indicators: Identify key indicators to measure the impact of the recommendations, such as the number of women accessing prenatal care, the percentage of deliveries attended by skilled birth attendants, or the reduction in maternal mortality rates.

3. Modeling: Use modeling techniques, such as mathematical modeling or simulation models, to simulate the impact of each recommendation on the identified indicators. This can involve creating scenarios with and without the implementation of the recommendations and estimating the potential changes in the indicators.

4. Data validation: Validate the model’s predictions by comparing them with real-world data or conducting pilot studies to assess the feasibility and effectiveness of the recommendations.

5. Analysis and interpretation: Analyze the simulated results to understand the potential impact of each recommendation on improving access to maternal health. Interpret the findings to inform decision-making and prioritize the most effective interventions.

6. Policy and implementation: Based on the simulation results, develop policies and strategies to implement the recommendations that have the highest potential for improving access to maternal health. Monitor and evaluate the implementation to assess the actual impact and make necessary adjustments.

It’s important to note that the specific methodology for simulating the impact may vary depending on the available data, resources, and context of the region being studied.

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