Summary Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast,...

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Background Sustainable Development Goal (SDG) 3 aims to "ensure healthy lives and promote well-being for all at all ages". While a substantial effort has been made to quantify progress towards SDG3, less research has focused on tracking spending towards this goal. We used spending estimates to measure progress in financing the priority areas of SDG3, examine the association between outcomes and financing, and identify where resource gains are most needed to achieve the SDG3 indicators for which data are available. MethodsWe estimated domestic health spending, disaggregated by source (government, out-of-pocket, and prepaid private) from 1995 to 2017 for 195 countries and territories. For disease-specific health spending, we estimated spending for HIV/AIDS and tuberculosis for 135 low-income and middle-income countries, and malaria in 106 malaria-endemic countries, from 2000 to 2017. We also estimated development assistance for health (DAH) from 1990 to 2019, by source, disbursing development agency, recipient, and health focus area, including DAH for pandemic preparedness. Finally, we estimated future health spending for 195 countries and territories from 2018 until 2030. We report all spending estimates in inflation-adjusted 2019 US$, unless otherwise stated. FindingsSince the development and implementation of the SDGs in 2015, global health spending has increased, reaching $7•9 trillion (95% uncertainty interval 7•8-8•0) in 2017 and is expected to increase to $11•0 trillion (10•7-11•2) by 2030. In 2017, in low-income and middle-income countries spending on HIV/AIDS was $20•2 billion (17•0-25•0) and on tuberculosis it was $10•9 billion (10•3-11•8), and in malaria-endemic countries spending on malaria was $5•1 billion (4•9-5•4). Development assistance for health was $40•6 billion in 2019 and HIV/AIDS has been the health focus area to receive the highest contribution since 2004. In 2019, $374 million of DAH was provided for pandemic preparedness, less than 1% of DAH. Although spending has increased across HIV/AIDS, tuberculosis, and malaria since 2015, spending has not increased in all countries, and outcomes in terms of prevalence, incidence, and per-capita spending have been mixed. The proportion of health spending from pooled sources is expected to increase from 81•6% (81•6-81•7) in 2015 to 83•1% (82•8-83•3) in 2030.Interpretation Health spending on SDG3 priority areas has increased, but not in all countries, and progress towards meeting the SDG3 targets has been mixed and has varied by country and by target. The evidence on the scale-up of spending and improvements in health outcomes suggest a nuanced relationship, such that increases in spending do not always results in improvements in outcomes. Although countries will probably need more resources to achieve SDG3, other constraints in the broader health system such as inefficient allocation of resources across interventions and populations, weak governance systems, human resource shortages, and drug shortages, will also need to be addressed.Funding The Bill & ...

ObjectivesAlthough pharmacogenetic tests provide the information on a genotype and the predicted phenotype, these tests do not themselves provide the interpretation of data for a physician. Currently, there are approximately two dozen pharmacogenomic clinical decision support systems (CDSSs) used in psychiatry. Implementation of the CDSSs forming the recommendations on drug and dose selection according to the results of pharmacogenetic testing is an urgent task. Fulfillment of this task will allow increasing the efficacy of therapy and decreasing the risk of undesirable side effects.MethodsThe study included 118 male patients (48 in the main group and 70 in the control group) with affective disorders and comorbid alcohol use disorder. To evaluate the efficacy and safety of therapy, several international psychometric scales and rating scales to measure side effects were used. Genotyping was performed using the real-time polymerase chain reaction with allele-specific hybridization. Pharmacogenetic testing results were interpreted using free software PGX2 (LLE Medicine, Russian Federation, Biomedical Cluster of Skolkovo, Moscow Innovative Cluster; www.pgx2.com).ResultsThe statistically significant differences across the scores on psychometric scales were revealed. For instance, the total score on the Hamilton Rating Scale for Depression by day 9 was 9.0 [8.0; 10.0] for the main group and 11.0 [10.0; 12.0] (p<0.001) for the control group and by day 16 it was 4.0 [2.0; 6.0] for the main group and 14.0 [13.0; 14.0] (p<0.001) for the control group. The UKU Side-Effect Rating Scale (UKU) also revealed a statistically significant difference. The total score on the UKU scale by day 9 was 4.0 [4.0; 5.0] for the main group and 5.0 [5.0; 6.0] (p<0.001) for the control group and by day 16 this difference grew significantly: 3.0 [0.0; 4.2] for the main group and 9.0 [7.0; 11.0] (p<0.001) for the control group.ConclusionsPharmacogenetic-guided personalization of the drug dose in patients with affective disorders and comorbid alcohol use disorder can reduce the risk of undesirable side effects and pharmacoresistance. It allows recommending the use of pharmacogenetic CDSSs for optimizing drug dosage.