The burden of mental disorders, substance use disorders and self-harm among young people in Europe, 1990–2019: Findings from the Global Burden of Disease Study 2019

The Lancet Regional Health - Europe, Volume 16, Year 2022

Interpretation

Background: Mental health is a public health issue for European young people, with great heterogeneity in resource allocation. Representative population-based studies are needed. The Global Burden of Disease (GBD) Study 2019 provides internationally comparable information on trends in the health status of populations and changes in the leading causes of disease burden over time. Methods: Prevalence, incidence, Years Lived with Disability (YLDs) and Years of Life Lost (YLLs) from mental disorders (MDs), substance use disorders (SUDs) and self-harm were estimated for young people aged 10-24 years in 31 European countries. Rates per 100,000 population, percentage changes in 1990-2019, 95% Uncertainty Intervals (UIs), and correlations with Sociodemographic Index (SDI), were estimated. Findings: In 2019, rates per 100,000 population were 16,983 (95% UI 12,823 – 21,630) for MDs, 3,891 (3,020 – 4,905) for SUDs, and 89·1 (63·8 – 123·1) for self-harm. In terms of disability, anxiety contributed to 647·3 (432–912·3) YLDs, while in terms of premature death, self-harm contributed to 319·6 (248·9–412·8) YLLs, per 100,000 population. Over the 30 years studied, YLDs increased in eating disorders (14·9%;9·4-20·1) and drug use disorders (16·9%;8·9-26·3), and decreased in idiopathic developmental intellectual disability (–29·1%;23·8-38·5). YLLs decreased in self-harm (–27·9%;38·3-18·7). Variations were found by sex, age-group and country. The burden of SUDs and self-harm was higher in countries with lower SDI, MDs were associated with SUDs. Interpretation: Mental health conditions represent an important burden among young people living in Europe. National policies should strengthen mental health, with a specific focus on young people. Funding: The Bill and Melinda Gates Foundation The Global Burden of Disease Study produces annual estimates on prevalence, incidence and mortality for 369 diseases and injuries. Each update incorporates new data and methodological improvements to provide stakeholders with the most up-to-date information for resource allocation decisions and are compliant with the Guidelines for Accurate and Transparent Health Estimates Reporting.14 The present study employed estimates from GBD 2019, which are available on the Global Health Data Exchange (GHDx).15 These estimates supersede those from previous rounds of GBD, since the estimates for the whole time series are updated on the basis of addition of new data and change in methods, where appropriate, at each iteration of the GBD study.3 Methods for the generation of GBD 2019 estimates are described in detail elsewhere,3 while the methodology for estimating the burden due to mental health conditions is briefly summarised here. We provided results from 1990 to 2019 for 31 European countries: the 28 EU countries (the UK was still part of EU), plus Iceland, Norway and Switzerland, as part of Schengen area. The catchment population comprised young people between 10 and 24 years old,16 with a total number of 85 million subjects in 2019. The estimates were based on data on incidence and prevalence identified through systematic searches of published and unpublished documents, survey microdata, administrative records of health encounters, registries, and disease surveillance system that are catalogued in the Global Health Data Exchange website (http://ghdx.healthdata.org). We summarized these data sources for the 31 countries of interest, related to MDs, SUDs and self-harm in the 10-24 age range, in Appendix (Overview on data coverage). We included the following measures of disease burden: prevalence (MDs and SUDs), incidence (self-harm,) YLDs, and YLLs. YLDs are years lived with disability (in which the disability equates to a fraction of a year lived in full health) and are the product of the prevalence and the disability weight of that condition. YLLs are years of life lost due to premature death, calculated as the difference between the corresponding standard life expectancy for that person’s age and sex, and the age of actual death. Disability-adjusted life years (DALYs) are the sum of YLDs and YLLs. DALYs were used only to provide the fraction of YLDs and YLLs for each disorder. Prevalence was derived from estimates of point prevalence for all MDs and SUDs, with the exception of bipolar disorders, where one-year prevalence was applied.17 We used prevalence estimates for all conditions which usually last more than six months. This involved also SUDs, even if a small degree of them also contributed also to premature deaths. We used incidence estimates for self-harm, since the great majority of the burden due to self-harm was represented by YLLs due to fatal self-harm.18 Prevalence and incidence were modelled using DisMod-MR 2.1, a Bayesian meta-regression tool. Epidemiological data from different sources were pooled by DisMod-MR 2.1 with the goal of producing internally consistent estimates of prevalence, incidence, remission, and excess mortality by age, sex, location, and year. Proportions of severity were calculated to reflect the different levels of disability, or sequelae, associated with a determinate disorder, eg, mild, moderate, and severe presentations. Severity proportions, as shown elsewhere,10 were applied to the total prevalent cases estimated by DisMod-MR 2.1 to obtain prevalence estimates for each level of severity. As described in detail in other studies based on GBD 2019,3,19 disability weights by condition were applied to estimate YLDs. These have been calculated through a series of severity splits, which definie the sequelae of a health condition as asymptomatic, mild, moderate, and severe. Disability weights derived from different international surveys, where a scale ranging from perfect health (0) to death (1) was used, adding also population health equivalence questions that compared the lifesaving benefits and the prevention programmes for several health states. The analysis of the surveys served for the relative position of health states to each other, while the population health equivalence questions were used to assess those relative positions as values on a scale ranging from 0 to 1. More information on the sequela-specific health state descriptions and on the disability weights analysis are described elsewhere.10 A simulation method based on simulated populations of individuals by location, age, sex, and year, was used to adjust for comorbidity, since the burden attributable to each cause in GBD was estimated separately. Individuals in each population were exposed to the independent probability of having a combination of different sequelae in GBD 2019. A comorbidity correction was then used to estimate the difference between the average disability weight of individuals experiencing one sequela and the multiplicatively combined disability weights of those experiencing more sequelae. Specific YLDs per location, age, sex, and year applied the average comorbidity correction calculated for each sequela.10 Uncertainty intervals (UIs) were used to describe the point estimates of uncertainty from model specification, stochastic variation, and measurement bias. UIs are based on 1000 draws from the posterior distribution of estimates. The point estimate is defined by the mean of the draws, while the the 95% UIs is represented by the 2·5th and 97·5th percentiles ranked estimates from the drawns. In GBD 2019, diseases and injuries and causes of death, were aggregated in three Level 1 causes (communicable, maternal, neonatal, and nutritional conditions; NCDs; and injuries), 22 Level 2 causes, 174 Level 3 causes, and 301 Level 4 causes.3 In this study, we included Level 2 (MDs and SUDs) and Level 3 causes, as follows: Only for MDs, we also aggregate disorders as follows, to describing prevalence rates among the 31 countries of interest: Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) or the International Classification of Diseases – Tenth revision (ICD-10) criteria were used for definition of cases,19 as they were used by the majority of mental health surveys included in the Appendix. the. SDI is a composite indicator of development status, built as the geometric mean of 0 to 1 indices of total fertility rate in women younger than 25 years, mean education for the population aged 15 years and older, and lag-distributed income per capita.3 We used the SDI for each of the 31 countries of this study. For each country, cause and year, we report count, age rates per 100,000 population for age subgroups, and percentage changes from 1990 to 2019 for estimates of prevalence, incidence, YLDs, and YLLs, with 95% UIs,3. The Institute for Health Metrics and Evaluation (IHME) provided aggregated estimates for all 31 countries combined, since the standard GBD aggregate estimates are for the EU, and exclude the other Schengen area countries (i.e. Iceland, Norway and Switzerland) .. Results are presented by sex and age subgroups (10-14; 15-19 and 20-24 years). YLLs were calculated only for self-harm, eating disorders, alcohol use disorders and substance use disorders since these are the only causes considered causes of death in the WHO/ICD-system (https://www.who.int/standards/classifications). We also reported the percentages of YLDs and YLLs of MDs, SUDs and self-harm in the 10-24 age groups compared to the all-causes GBD in the 31 European countries. In addition, we performed Spearman rank-correlations to study the relation between SDI and prevalence rates of MDs and SUDs, and incidence rates of self-harm. We set P-value <0·05 as the threshold of statistical significance. These analyses were conducted with Stata/BE 17.0 (StataCorp LLC, College Station, USA). The funder of the study had no role in study design, data collection, analysis and 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 for publication.

AI Digest

Study Justification:

– Mental health is a significant public health issue among young people in Europe.
– Representative population-based studies are needed to understand the burden of mental disorders, substance use disorders, and self-harm.
– The Global Burden of Disease (GBD) Study 2019 provides internationally comparable information on trends in the health status of populations and changes in the leading causes of disease burden over time.

Study Highlights:

– The study estimated the prevalence, incidence, Years Lived with Disability (YLDs), and Years of Life Lost (YLLs) from mental disorders, substance use disorders, and self-harm among young people aged 10-24 years in 31 European countries.
– In 2019, the rates per 100,000 population were 16,983 for mental disorders, 3,891 for substance use disorders, and 89.1 for self-harm.
– Anxiety contributed to the highest YLDs, while self-harm contributed to the highest YLLs.
– Over the 30-year period studied, YLDs increased in eating disorders and drug use disorders, and decreased in idiopathic developmental intellectual disability. YLLs decreased in self-harm.
– Variations were found by sex, age-group, and country, with higher burden in countries with lower development status.

Study Recommendations:

– National policies should prioritize and strengthen mental health, with a specific focus on young people.
– Resource allocation should be directed towards addressing the burden of mental disorders, substance use disorders, and self-harm among young people.
– Interventions should target specific conditions such as anxiety, eating disorders, and drug use disorders.
– Efforts should be made to reduce self-harm rates and improve mental health outcomes among young people.

Key Role Players:

– Policy makers and government agencies responsible for health and mental health policies.
– Mental health professionals, including psychiatrists, psychologists, and counselors.
– Healthcare providers and facilities, including hospitals and clinics.
– Educational institutions, including schools and universities.
– Non-governmental organizations (NGOs) and community-based organizations working in the field of mental health.

Cost Items for Planning Recommendations:

– Funding for mental health programs and services, including prevention, early intervention, and treatment.
– Training and capacity building for mental health professionals.
– Development and implementation of mental health promotion campaigns.
– Research and data collection on mental health among young people.
– Integration of mental health services into existing healthcare systems.
– Support for community-based initiatives and support networks.
– Evaluation and monitoring of mental health interventions and outcomes.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides detailed information on the methods used, including data sources and statistical analysis. However, it does not mention specific limitations or potential biases in the study. To improve the evidence, the abstract could include a brief discussion of limitations and potential areas for future research.

Abstract

The Global Burden of Disease Study produces annual estimates on prevalence, incidence and mortality for 369 diseases and injuries. Each update incorporates new data and methodological improvements to provide stakeholders with the most up-to-date information for resource allocation decisions and are compliant with the Guidelines for Accurate and Transparent Health Estimates Reporting.14 The present study employed estimates from GBD 2019, which are available on the Global Health Data Exchange (GHDx).15 These estimates supersede those from previous rounds of GBD, since the estimates for the whole time series are updated on the basis of addition of new data and change in methods, where appropriate, at each iteration of the GBD study.3 Methods for the generation of GBD 2019 estimates are described in detail elsewhere,3 while the methodology for estimating the burden due to mental health conditions is briefly summarised here. We provided results from 1990 to 2019 for 31 European countries: the 28 EU countries (the UK was still part of EU), plus Iceland, Norway and Switzerland, as part of Schengen area. The catchment population comprised young people between 10 and 24 years old,16 with a total number of 85 million subjects in 2019. The estimates were based on data on incidence and prevalence identified through systematic searches of published and unpublished documents, survey microdata, administrative records of health encounters, registries, and disease surveillance system that are catalogued in the Global Health Data Exchange website (http://ghdx.healthdata.org). We summarized these data sources for the 31 countries of interest, related to MDs, SUDs and self-harm in the 10-24 age range, in Appendix (Overview on data coverage). We included the following measures of disease burden: prevalence (MDs and SUDs), incidence (self-harm,) YLDs, and YLLs. YLDs are years lived with disability (in which the disability equates to a fraction of a year lived in full health) and are the product of the prevalence and the disability weight of that condition. YLLs are years of life lost due to premature death, calculated as the difference between the corresponding standard life expectancy for that person’s age and sex, and the age of actual death. Disability-adjusted life years (DALYs) are the sum of YLDs and YLLs. DALYs were used only to provide the fraction of YLDs and YLLs for each disorder. Prevalence was derived from estimates of point prevalence for all MDs and SUDs, with the exception of bipolar disorders, where one-year prevalence was applied.17 We used prevalence estimates for all conditions which usually last more than six months. This involved also SUDs, even if a small degree of them also contributed also to premature deaths. We used incidence estimates for self-harm, since the great majority of the burden due to self-harm was represented by YLLs due to fatal self-harm.18 Prevalence and incidence were modelled using DisMod-MR 2.1, a Bayesian meta-regression tool. Epidemiological data from different sources were pooled by DisMod-MR 2.1 with the goal of producing internally consistent estimates of prevalence, incidence, remission, and excess mortality by age, sex, location, and year. Proportions of severity were calculated to reflect the different levels of disability, or sequelae, associated with a determinate disorder, eg, mild, moderate, and severe presentations. Severity proportions, as shown elsewhere,10 were applied to the total prevalent cases estimated by DisMod-MR 2.1 to obtain prevalence estimates for each level of severity. As described in detail in other studies based on GBD 2019,3,19 disability weights by condition were applied to estimate YLDs. These have been calculated through a series of severity splits, which definie the sequelae of a health condition as asymptomatic, mild, moderate, and severe. Disability weights derived from different international surveys, where a scale ranging from perfect health (0) to death (1) was used, adding also population health equivalence questions that compared the lifesaving benefits and the prevention programmes for several health states. The analysis of the surveys served for the relative position of health states to each other, while the population health equivalence questions were used to assess those relative positions as values on a scale ranging from 0 to 1. More information on the sequela-specific health state descriptions and on the disability weights analysis are described elsewhere.10 A simulation method based on simulated populations of individuals by location, age, sex, and year, was used to adjust for comorbidity, since the burden attributable to each cause in GBD was estimated separately. Individuals in each population were exposed to the independent probability of having a combination of different sequelae in GBD 2019. A comorbidity correction was then used to estimate the difference between the average disability weight of individuals experiencing one sequela and the multiplicatively combined disability weights of those experiencing more sequelae. Specific YLDs per location, age, sex, and year applied the average comorbidity correction calculated for each sequela.10 Uncertainty intervals (UIs) were used to describe the point estimates of uncertainty from model specification, stochastic variation, and measurement bias. UIs are based on 1000 draws from the posterior distribution of estimates. The point estimate is defined by the mean of the draws, while the the 95% UIs is represented by the 2·5th and 97·5th percentiles ranked estimates from the drawns. In GBD 2019, diseases and injuries and causes of death, were aggregated in three Level 1 causes (communicable, maternal, neonatal, and nutritional conditions; NCDs; and injuries), 22 Level 2 causes, 174 Level 3 causes, and 301 Level 4 causes.3 In this study, we included Level 2 (MDs and SUDs) and Level 3 causes, as follows: Only for MDs, we also aggregate disorders as follows, to describing prevalence rates among the 31 countries of interest: Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) or the International Classification of Diseases – Tenth revision (ICD-10) criteria were used for definition of cases,19 as they were used by the majority of mental health surveys included in the Appendix. the. SDI is a composite indicator of development status, built as the geometric mean of 0 to 1 indices of total fertility rate in women younger than 25 years, mean education for the population aged 15 years and older, and lag-distributed income per capita.3 We used the SDI for each of the 31 countries of this study. For each country, cause and year, we report count, age rates per 100,000 population for age subgroups, and percentage changes from 1990 to 2019 for estimates of prevalence, incidence, YLDs, and YLLs, with 95% UIs,3. The Institute for Health Metrics and Evaluation (IHME) provided aggregated estimates for all 31 countries combined, since the standard GBD aggregate estimates are for the EU, and exclude the other Schengen area countries (i.e. Iceland, Norway and Switzerland) .. Results are presented by sex and age subgroups (10-14; 15-19 and 20-24 years). YLLs were calculated only for self-harm, eating disorders, alcohol use disorders and substance use disorders since these are the only causes considered causes of death in the WHO/ICD-system (https://www.who.int/standards/classifications). We also reported the percentages of YLDs and YLLs of MDs, SUDs and self-harm in the 10-24 age groups compared to the all-causes GBD in the 31 European countries. In addition, we performed Spearman rank-correlations to study the relation between SDI and prevalence rates of MDs and SUDs, and incidence rates of self-harm. We set P-value <0·05 as the threshold of statistical significance. These analyses were conducted with Stata/BE 17.0 (StataCorp LLC, College Station, USA). The funder of the study had no role in study design, data collection, analysis and 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 for publication.

Materials

Innovations

I’m sorry, but I’m unable to provide specific innovations for improving access to maternal health based on the information you provided. Could you please provide more specific details or context regarding the innovations you are looking for?

AI Innovations Description

The provided text appears to be a description of a study on the burden of mental disorders, substance use disorders, and self-harm among young people in Europe. It discusses the methodology used to estimate the prevalence, incidence, Years Lived with Disability (YLDs), and Years of Life Lost (YLLs) for these conditions. The study highlights the importance of mental health among young people in Europe and suggests that national policies should focus on strengthening mental health services for this population.

To develop this study into an innovation to improve access to maternal health, the following recommendation could be considered:

1. Implement integrated mental health services within maternal health programs: Recognizing the significant burden of mental health conditions among young people, including pregnant women and new mothers, it is important to integrate mental health services into existing maternal health programs. This can be achieved by training healthcare providers to identify and address mental health issues during pregnancy and the postpartum period. Additionally, providing access to mental health professionals, such as psychologists or counselors, within maternal health clinics or through telemedicine platforms can help improve access to mental health support for pregnant women and new mothers.

By integrating mental health services into maternal health programs, pregnant women and new mothers can receive comprehensive care that addresses both their physical and mental well-being. This approach can help improve maternal health outcomes and ensure that women have the support they need during this critical period in their lives.

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 care, postpartum support, and consultations. This can be especially beneficial for women in rural or underserved areas who may have limited access to healthcare facilities.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information on prenatal care, nutrition, and self-care during pregnancy can empower women to take control of their health. These apps can also send reminders for appointments and medication schedules.

3. Community health workers: Training and deploying community health workers can help bridge the gap between healthcare facilities and pregnant women. These workers can provide education, support, and referrals for maternal health services within their communities.

4. Transportation services: Lack of transportation can be a barrier to accessing maternal health services. Implementing transportation services, such as shuttle buses or vouchers for rideshare services, can ensure that pregnant women can easily reach healthcare facilities.

5. Maternal health clinics: Establishing specialized maternal health clinics can provide comprehensive care for pregnant women, including prenatal care, childbirth services, and postpartum support. These clinics can be equipped with necessary resources and staffed by skilled healthcare professionals.

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

1. Define the target population: Identify the specific population group that will be impacted by the recommendations, such as pregnant women in a particular region or community.

2. Collect baseline data: Gather data on the current state of access to maternal health services in the target population. This can include information on the number of healthcare facilities, distance to facilities, utilization rates, and any existing barriers.

3. Model the impact of recommendations: Use modeling techniques to simulate the potential impact of each recommendation on improving access to maternal health. This can involve estimating the increase in the number of women who will have access to care, reduction in travel time or distance to healthcare facilities, and improvements in health outcomes.

4. Consider contextual factors: Take into account contextual factors that may influence the effectiveness of the recommendations, such as cultural beliefs, socioeconomic status, and existing healthcare infrastructure. Adjust the simulation accordingly to reflect these factors.

5. Analyze the results: Evaluate the simulated impact of the recommendations on access to maternal health services. Assess the potential benefits, challenges, and cost-effectiveness of each recommendation.

6. Refine and iterate: Based on the analysis, refine the recommendations and simulation methodology as needed. Iterate the process to further optimize the strategies for improving access to maternal health.

By following this methodology, policymakers and healthcare stakeholders can make informed decisions on which recommendations to prioritize and implement to effectively improve access to maternal health.

Author

Dima Health

Statistics:

Citations: 25

Identifiers:

DOI: 10.1016/j.lanepe.2022.100341

Research Areas:

Community Interventions, Disability, Disparities, Genetics and Genomics, Health System and Policy, Maternal Access, Maternal and Child Health, Mental Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Substance Abuse, Violence and Injury

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female