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.

Comprehensive and comparable estimates of health spending in each country are a key input for health policy and planning, and are necessary to support the achievement of national and international health goals. Previous studies have tracked past and projected future health spending until 2040 and shown that, with economic development, countries tend to spend more on health per capita, with a decreasing share of spending from development assistance and out-of-pocket sources. We aimed to characterise the past, present, and predicted future of global health spending, with an emphasis on equity in spending across countries. We estimated domestic health spending for 195 countries and territories from 1995 to 2016, split into three categories-government, out-of-pocket, and prepaid private health spending-and estimated development assistance for health (DAH) from 1990 to 2018. We estimated future scenarios of health spending using an ensemble of linear mixed-effects models with time series specifications to project domestic health spending from 2017 through 2050 and DAH from 2019 through 2050. Data were extracted from a broad set of sources tracking health spending and revenue, and were standardised and converted to inflation-adjusted 2018 US dollars. Incomplete or low-quality data were modelled and uncertainty was estimated, leading to a complete data series of total, government, prepaid private, and out-of-pocket health spending, and DAH. Estimates are reported in 2018 US dollars, 2018 purchasing-power parity-adjusted dollars, and as a percentage of gross domestic product. We used demographic decomposition methods to assess a set of factors associated with changes in government health spending between 1995 and 2016 and to examine evidence to support the theory of the health financing transition. We projected two alternative future scenarios based on higher government health spending to assess the potential ability of governments to generate more resources for health. Between 1995 and 2016, health spending grew at a rate of 4·00% (95% uncertainty interval 3·89-4·12) annually, although it grew slower in per capita terms (2·72% [2·61-2·84]) and increased by less than $1 per capita over this period in 22 of 195 countries. The highest annual growth rates in per capita health spending were observed in upper-middle-income countries (5·55% [5·18-5·95]), mainly due to growth in government health spending, and in lower-middle-income countries (3·71% [3·10-4·34]), mainly from DAH. Health spending globally reached $8·0 trillion (7·8-8·1) in 2016 (comprising 8·6% [8·4-8·7] of the global economy and $10·3 trillion [10·1-10·6] in purchasing-power parity-adjusted dollars), with a per capita spending of US$5252 (5184-5319) in high-income countries, $491 (461-524) in upper-middle-income countries, $81 (74-89) in lower-middle-income countries, and $40 (38-43) in low-income countries. In 2016, 0·4% (0·3-0·4) of health spending globally was in low-income countries, despite these countries comprising 10·0% of the global population. In 2018, the largest proportion of DAH targeted HIV/AIDS ($9·5 billion, 24·3% of total DAH), although spending on other infectious diseases (excluding tuberculosis and malaria) grew fastest from 2010 to 2018 (6·27% per year). The leading sources of DAH were the USA and private philanthropy (excluding corporate donations and the Bill & Melinda Gates Foundation). For the first time, we included estimates of China's contribution to DAH ($644·7 million in 2018). Globally, health spending is projected to increase to $15·0 trillion (14·0-16·0) by 2050 (reaching 9·4% [7·6-11·3] of the global economy and $21·3 trillion [19·8-23·1] in purchasing-power parity-adjusted dollars), but at a lower growth rate of 1·84% (1·68-2·02) annually, and with continuing disparities in spending between countries. In 2050, we estimate that 0·6% (0·6-0·7) of health spending will occur in currently low-income countries, despite these countries comprising an estimated 15·7% of the global population by 2050. The ratio between per capita health spending in high-income and low-income countries was 130·2 (122·9-136·9) in 2016 and is projected to remain at similar levels in 2050 (125·9 [113·7-138·1]). The decomposition analysis identified governments' increased prioritisation of the health sector and economic development as the strongest factors associated with increases in government health spending globally. Future government health spending scenarios suggest that, with greater prioritisation of the health sector and increased government spending, health spending per capita could more than double, with greater impacts in countries that currently have the lowest levels of government health spending. Financing for global health has increased steadily over the past two decades and is projected to continue increasing in the future, although at a slower pace of growth and with persistent disparities in per-capita health spending between countries. Out-of-pocket spending is projected to remain substantial outside of high-income countries. Many low-income countries are expected to remain dependent on development assistance, although with greater government spending, larger investments in health are feasible. In the absence of sustained new investments in health, increasing efficiency in health spending is essential to meet global health targets. Bill & Melinda Gates Foundation.

Best practices, policy and innovations in the administration of healthcare in developing communities and countries. For administrators, academics, researchers and policy leaders. Includes peer reviewed research papers. Edited by Dr. Judith Shamian, President of the International Council of Nurses, Geneva CH

Aims: We investigate the effect of motivational interviewing (MI), delivered in a brief intervention during an emergency care contact, on the alcohol consumption of young people who screen positively for present or previous risky alcohol consumption. Methods: MEDLINE, CINAHL, EMBASE, PsycARTICLES, PsycINFO, PSYNDEX and Scopus were searched for randomized controlled trials with adolescents or young adults that compared MI in an emergency care setting to control conditions and measured drinking outcomes. Results: Six trials with 1433 participants, aged 13–25 years, were included in the systematic review and meta-analysis. MI was never less efficacious than a control intervention. Two trials found significantly more reduction in one or more measures of alcohol consumption in the MI intervention group. One trial indicated that MI may be used most effectively in young people with high-volume alcohol consumption. Separate random effects meta-analyses were performed based on the highest impact that MI added on reducing the drinking frequency and the drinking quantity at any point in time during the different study periods. Their results were expressed as standardized mean differences (SMDs). The frequency of drinking alcohol decreased significantly more after MI than after control interventions (SMD ≤ −0.17, P ≤ 0.03). In addition, MI reduced the drinking quantity further than control interventions in a meta-analysis of the subset of trials that were implemented in the USA (SMD = −0.12, P = 0.04). Meta-analyses of the smallest mean differences between MI and control groups detected no differences in alcohol use (SMD ≤ 0.02, P ≥ 0.38). Conclusion: MI appears at least as effective and may possibly be more effective than other brief interventions in emergency care to reduce alcohol consumption in young people.

There has been little evaluation of the postpartum quality of life (QOL) of women in India and its association with the mode of birth. This study piloted the use of the generic EQ-5D-5L questionnaire to assess postpartum QOL experienced by rural Indian women. A convenience sample of rural women who gave birth in a health facility in Gujarat or Madhya Pradesh was recruited into this pilot study. QOL was measured during three interviews within 30 days of birth using the EQ-5D-5L questionnaire. Patient-level quality-adjusted life days (QALDs) were estimated. Multivariate regression was used to adjust for selected baseline characteristics. Forty-six women with cesarean section and 178 with vaginal birth from 17 public and private health facilities were studied. Postpartum QOL in both groups improved between interviews 1 and 3. Comparing between vaginal and cesarean births indicated that the vaginal birth group had a higher QOL (0–3 days postpartum: 0.28 vs. 0.57, 3–7 days postpartum: 0.59 vs. 0.81; P < 0.001) and was more likely to report no or slight problems in 4 of 5 health dimensions (mobility, self-care, usual activities, pain or discomfort; P ≤ 0.04) during interviews 1 and 2. Postpartum QOL converged, but still differed between groups by the time of interview 3 (21–30 days postpartum: 0.85 vs. 0.93; P < 0.001). While most women reported no problems by the end of the first postpartum month, the difference in the ability to perform usual activities persisted (P = 0.001). In result, fewer QALDs were attained by women in the cesarean section group between day 1 and day 21 postpartum (13.1 vs. 16.6 QALDs; P < 0.001). Subgroup analysis showed that having had an episiotomy during vaginal birth was also associated with reduced QOL postpartum, but to a lesser extent than cesarean section. Similar results were obtained when adjusting for socioeconomic, pregnancy and birth characteristics, but postpartum QOL already ceased to be statistically different between groups before interview 3. Vaginal births, even with episiotomy, were associated with a higher postpartum QOL than cesarean births among the Indian women in our pilot study. Finding these expected results suggests that the EQ-5D-5L questionnaire is a suitable instrument to assess postpartum QOL in Indian women.

Urbanicity has been described as a risk factor for mental disorders. Findings differ across countries and psychiatric outcomes. Our aim was to systematically review quantitative studies of the relationship between urbanicity and prevalent mental disorders in Europe. EBSCOhost and PubMed databases were searched for epidemiological studies of European populations, published in English between January 2002 and October 2012, using the combination of keywords (urban* OR environment*) AND (mental health OR mental disorder OR psych*). The eleven studies included in the review used studies used different measures of urbanicity. The types of mental disorders most often examined, on which we focus in the review, were mood and anxiety disorders, psychosis, and substance use disorders. Urbanicity was associated with mental health. Seven out of nine studies reported more mood and anxiety disorders in some of the urban areas compared to rural areas. Two out of three studies indicated higher rates of psychosis in some more urbanised areas. Four out of six studies found more substance abuse with increased urbanicity. The same studies neither found any evidence for a relationship between urbanicity and mental disorders in several instances. Living in an urban environment in Europe can be a risk factor for mood and anxiety disorders, psychotic disorders, and substance abuse. More research is needed to understand which urban environment characteristics cause mental disorders.