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. 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. 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, most other GBD super-regions had slowed rates of progress in 2010-2019 relative to 1990-2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6-421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0-3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5-1040·3]) residing in south Asia. The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people-the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close-or how far-all populations are in benefiting from UHC. Bill & Melinda Gates Foundation.

Introduction: Although pharmacogenetic tests provide the information on a genotype and the predicted phenotype, these tests themselves do not provide the interpretation of data for a physician. There are currently approximately two dozen pharmacogenomic clinical decision support systems used in psychiatry. Implementation of clinical decision support systems capable of forming recommendations on drug and dose selection according to the results of pharmacogenetic testing is an urgent task. Fulfillment of this task may allow increasing the efficacy of therapy and decreasing the risk of undesirable side effects. Materials and methods: The study included 51 male patients (21 in the main group and 30 in the control group) with alcohol withdrawal syndrome. 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 real‐time polymerase chain reaction with allele‐specific hybridization. Pharmacogenetic test results were interpreted using free software PGX2 ( www.pgx2.com ). Results: Statistically significant differences between the scores derived from all psychometric scales were revealed. For instance, the total score on CIWA‐Ar scale by day 3 was 13.5 [11.2; 16.0] for the main group and 18.0 [17.0; 22.0] ( p < 0.001) for the control group; by day 5, it was 6.5 [4.2; 8.0] for the main group and 15.0 [14.0; 16.0] ( p < 0.001) for the control group. The UKU side effect rating scale (UKU) also revealed a statistically significant difference. The total score on UKU scale by day 3 was 6.0 [5.0; 7.0] for the main group and 7.0 [6.0; 8.0] ( p < 0.001) for the control group; by day 5, this difference grew significantly: 5.5 [3.0; 9.0] for the main group and 14.0 [12.0; 19.0] ( p < 0.001) for the control group. The groups were representative (there was no difference between the scores at the inclusion of patients). Conclusion: Pharmacogenetic‐guided personalization of drug dose in patients with alcohol withdrawal syndrome can reduce the risk of undesirable side effects and pharmacoresistance. It allows recommending the use of pharmacogenomic clinical decision support systems for optimizing drug dosage.

Background: Today, it is proved that isoenzymes CYP2D6 and CYP3A4 are involved in metabolism of haloperidol. In our previous investigation, we found a medium correlation between the efficacy and safety of haloperidol and the activity of CYP3A4 in patients with alcohol abuse. Objective: The aim of this study was to evaluate the correlation between the activity of CYP2D6 and the efficacy and safety of haloperidol in patients with diagnosed alcohol abuse. Methods: The study involved 70 men (average age: 40.83±9.92 years) with alcohol addiction. A series of psychometric scales were used in the research. The activity of CYP2D6 was evaluated by high-performance liquid chromatography with mass spectrometry using the ratio of 6-hydroxy-1,2,3,4-tetrahydro-beta-carboline to pinoline. Genotyping of CYP2D6 (1846G>A) was performed using real-time polymerase chain reaction. Results: According to results of correlation analysis, statistically significant values of Spearman correlation coefficient (r_s) between the activity of CYP2D6 and the difference of points in psychometric scale were obtained in patients receiving haloperidol in injection form (Sheehan Clinical Anxiety Rating Scale =-0.721 [P<0.001] and Udvald for Kliniske Undersogelser Side Effect Rating Scale =0.692 [P<0.001]) and in those receiving haloperidol in tablet form (Covi Anxiety Scale =-0.851 [P<0.001] and Udvald for Kliniske Undersogelser Side Effect Rating Scale =0.797 [P<0.001]). Conclusion: This study demonstrated the correlations between the activity of CYP2D6 isozyme and the efficacy and safety of haloperidol in patients with alcohol addiction.

Background: Fluvoxamine therapy is used for treatment of patients with depressive disorder, but it is often ineffective, and some patients suffer from dose-dependent undesirable side effects such as vertigo, headache, indigestion, xerostomia, increased anxiety, etc. CYP2D6 is involved in the biotransformation of fluvoxamine. Meanwhile, the genes encoding these isoenzymes have a high level of polymorphism, which may affect the protein synthesis. Objective: The primary objective of our study was to investigate the effects of CYP2D6 genetic polymorphisms on the efficacy and safety of fluvoxamine in patients with depressive disorder and comorbid alcohol use disorder, in order to develop the algorithms of optimization of fluvoxamine therapy for reducing the risk of dose-dependent undesirable side effects and pharmacoresistance. Methods: The study involved 45 male patients (average age: 36.44±9.96 years) with depressive disorder and comorbid alcohol use disorder. A series of psychometric scales was used in the research. Genotyping of CYP2D6 (1846G>A) was performed using real-time polymerase chain reaction. Results: According to results of Mann–Whitney U-test, statistically significant differences between the efficacy and safety of fluvoxamine were obtained on 9th and 16th days of therapy in patients with GG and GA genotypes (The Hamilton Rating Scale for Depression: 10.0 [10.0; 23.0] vs 25.0 [24.0; 16.0] (P<0.001) on the 9th day and 4.0 [2.0; 5.0] vs 6.0 [6.0; 7.0] on the 16th day; The UKU Side Effect Rating Scale: 6.0 [4.0; 6.0] vs 9.0 [9.0; 10.0] (P<0.001) on the 9th day and 5.0 [1.0; 9.0] vs 19.0 [18.0; 22.0] on the 16th day). Conclusion: This study demonstrated the lower efficacy and safety of fluvoxamine in patients with depressive disorder and comorbid alcohol use disorders with GA genotype in CYP2D6 1846G>A polymorphic marker.

The objective of the study was to investigate the effects of CYP2D6 activity on the efficacy and safety of mirtazapine in patients with depressive disorders and comorbid alcohol use disorder who received mirtazapine. The study included 109 Russian patients who received mirtazapine at a dose of 30.0 [15.0; 45.0] mg per day. Genotyping of CYP2D6*4 (1846G > A, rs3892097) was performed using real-time polymerase chain reaction with allele-specific hybridization. The activity of CYP2D6 was evaluated by determining the concentration of endogenous substrate of the enzyme and its urinary metabolite — pinoline to 6-hydroxy-1,2,3,4-tetrahydro-beta-carboline ratio, using high-performance liquid chromatography – mass spectrometry. The statistically significant differences between the scores on the Hamilton Depression Rating Scale (HAMD) in patients with different genotypes were revealed by day 16: (GG) 5.0 [3.0; 6.0], (GA) 1.5 [1.0; 3.2] (p < 0.001), and for the The UKU Side Effects Rating Scale (UKU): (GG) 6.0 [6.0; 7.0], (GA) 8.5 [8.0; 10.0] (p < 0.001). The calculation of correlation coefficients between the differences in scale scores and metabolic rate showed the presence of statistically significant weak inverse correlation with the efficacy indicator evaluated by HAMD (r = −0.278, p < 0.05), but not by UKU (r = 0.274, p > 0.05). This study demonstrated that an increased CYP2D6 activity reduces the efficacy of treatment with mirtazapine.