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,...

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3•5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.

Background Gastro-oesophageal reflux disease is a common chronic ailment that causes uncomfortable symptoms and increases the risk of oesophageal adenocarcinoma. We aimed to report the burden of gastro-oesophageal reflux disease in 195 countries and territories between 1990 and 2017, using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017. Methods We did a systematic review to identify measurements of the prevalence of gastro-oesophageal reflux disease in geographically defined populations worldwide between 1990 and 2017. These estimates were analysed with DisMod-MR, a Bayesian mixed-effects meta-regression tool that incorporates predictive covariates and adjustments for differences in study design in a geographical cascade of models. Fitted values for broader geographical units inform prior distributions for finer geographical units. Prevalence was estimated for 195 countries and territories. Reports of the frequency and severity of symptoms among individuals with gastrooesophageal reflux disease were used to estimate the prevalence of cases with no, mild to moderate, or severe to very severe symptoms at a given time; these estimates were multiplied by disability weights to estimate years lived with disability (YLD). Findings Data to estimate gastro-oesophageal reflux disease burden were scant, totalling 144 location-years (unique measurements from a year and location, regardless of whether a study reported them alongside measurements for other locations or years) of prevalence data. These came from six (86%) of seven GBD super-regions, 11 (52%) of 21 GBD regions, and 39 (20%) of 195 countries and territories. Mean estimates of age-standardised prevalence for all locations in 2017 ranged from 4408 cases per 100 000 population to 14 035 cases per 100 000 population. Agestandardised prevalence was highest (>11 000 cases per 100 000 population) in the USA, Italy, Greece, New Zealand, and several countries in Latin America and the Caribbean, north Africa and the Middle East, and eastern Europe; it was lowest (<7000 cases per 100 000 population) in the high-income Asia Pacific, east Asia, Iceland, France, Denmark, and Switzerland. Global prevalence peaked at ages 75-79 years, at 18 820 (95% uncertainty interval [95% UI] 13 770-24 000) cases per 100 000 population. Global age-standardised prevalence was stable between 1990 and 2017 (8791 [95% UI 7772-9834] cases per 100 000 population in 1990 and 8819 [7781-9863] cases per 100 000 population in 2017, percentage change 0•3% [-0•3 to 0•9]), but all-age prevalence increased by 18

Background Despite an increased number of infants born with macrosomia globally, low birth weight infants have currently attracted more attention. Macrosomia is a growing problem in most developing countries and it directly or indirectly contributes to morbidity, mortality, and disability worldwide. The main objective of this study was to assess the level of macrosomia and its associated factors in the private clinics of Mekelle city, Tigray region, Ethiopia, 2017. Methods An institution based cross-sectional study with a total of 309 pregnant mothers was conducted. We collected data from the pregnant mothers as well as from their medical records using structured questionnaire and checklist respectively. We entered and analyzed the data using statistical package for social science (SPSS)-21 by applying binary logistic regression to identify the factors associated with macrosomia. Finally, we used texts and tables to summarize the results of the study. Results The prevalence of macrosomia was 19.1% (95% confidence interval (CI) = 14.9, 23), and the mean ± standard deviations of birth weights were 3440 ± 543 g. Macrosomia was significantly associated with: weight gain during pregnancy ≥16 kg (adjusted odds ratio (AOR) = 11, 95% CI: 3, 37), pre-pregnancy overweight (AOR = 5, 95% CI = 2, 13), pre-pregnancy obesity (AOR = 15, 95% CI = 5, 50), maternal age (AOR =2.6, 95% CI = 1.2, 5.8) and giving birth to macrosomic baby in the last pregnancy (AOR = 2.7, 95% CI = 1.1, 7). Conclusion We found that prevalence of macrosomia was high, and significantly associated with pre-pregnancy body mass index (BMI), pregnancy weight gain, maternal age and giving birth to a macrosomic baby in the last pregnancy. Hence, we recommend that emphasis should be given to maternal counseling for weight management before and during pregnancy.

IntroductionWeight gain during pregnancy is an important indicator of maternal and fetal nutrition during pregnancy. However, information regarding the effect of pregnancy weight gain on birth weight is lacking from developing countries.ObjectiveTo determine the effect of pregnancy weight gain on the newborn’s birth weight in mothers attending antenatal care (ANC) services from private clinics.MethodsHealth facility-based follow-up study was conducted among 332 pregnant mothers attending their antenatal care in Mekelle city, from October 2016 to June 2017. Before 28 weeks of gestation, pregnancy weight was collected retrospectively, then, mothers were followed-up until the time of infant delivery to record their birth weight. Data were also collected by a structured questionnaire and checklists and analyzed using SPSS version 21. The relationship between dependent and independent variables was assessed and presented using descriptive statistics, as well as t-test, ANOVA, and multivariable linear regression analysis. Variables:—pre-pregnancy BMI, maternal age, parity, decision making power on monetary resources, pregnancy interval, availability of housemaid, women dietary diversity score, maternal occupation, and pregnancy weight gain were included in the multivariable analysis.ResultsMaternal weight increased monthly at a mean ± SD rate of 2 ± 0.7 kg in the second trimester, and 1.5 ± 0.7 kg in the third trimester. The mean ± SD of pre-pregnancy body mass index (BMI) and total pregnancy weight gain was 23.8 ± 4.6 kg/m2, and 12 ± 2.8 kg respectively. The mean ± SD of birth weight was 3440 ± 542 grams. Weight gain has a significant effect on infant birth weight, a 1 kg increase in the pregnancy weight was associated with 94 g increase in BW (β = 97, 95% CI: 73–120). After dividing the pre-pregnancy weight into four groups (< 18.5, 18.5–24.9, 25–29.9 and ≥30) kg/m2 based on the Institute of Medicine (IOM), we found a statistically significant birth weight difference between each group.ConclusionPregnancy weight gain has a significant effect on birth weight. Thus, ANC counseling services should focus on maternal weight gain to prevent sub-optimal birth weight.