Summary Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. Findings Globally, 18·7% (95% uncertainty interval 18·4–19·0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58·8% (58·2–59·3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48·1 years (46·5–49·6) to 70·5 years (70·1–70·8) for men and from 52·9 years (51·7–54·0) to 75·6 years (75·3–75·9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49·1 years (46·5–51·7) for men in the Central African Republic to 87·6 years (86·9–88·1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216·0 deaths (196·3–238·1) per 1000 livebirths in 1950 to 38·9 deaths (35·6–42·83) per 1000 livebirths in 2017, with huge reductions acro...

SummaryBackgroundNational levels of personal health-care access and quality can be approximated by measuring mortality rates from causes that should not be fatal in the presence of effective medical care (ie, amenable mortality). Previous analyses of mortality amenable to health care only focused on high-income countries and faced several methodological challenges. In the present analysis, we use the highly standardised cause of death and risk factor estimates generated through the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to improve and expand the quantification of personal health-care access and quality for 195 countries and territories from 1990 to 2015.MethodsWe mapped the most widely used list of causes amenable to personal health care developed by Nolte and McKee to 32 GBD causes. We accounted for variations in cause of death certification and misclassifications through the extensive data standardisation processes and redistribution algorithms developed for GBD. To isolate the effects of personal health-care access and quality, we risk-standardised cause-specific mortality rates for each geography-year by removing the joint effects of local environmental and behavioural risks, and adding back the global levels of risk exposure as estimated for GBD 2015. We employed principal component analysis to create a single, interpretable summary measure–the Healthcare Quality and Access (HAQ) Index–on a scale of 0 to 100. The HAQ Index showed strong convergence validity as compared with other health-system indicators, including health expenditure per capita (r=0·88), an index of 11 universal health coverage interventions (r=0·83), and human resources for health per 1000 (r=0·77). We used free disposal hull analysis with bootstrapping to produce a frontier based on the relationship between the HAQ Index and the Socio-demographic Index (SDI), a measure of overall development consisting of income per capita, average years of education, and total fertility rates. This frontier allowed us to better quantify the maximum levels of personal health-care access and quality achieved across the development spectrum, and pinpoint geographies where gaps between observed and potential levels have narrowed or widened over time.FindingsBetween 1990 and 2015, nearly all countries and territories saw their HAQ Index values improve; nonetheless, the difference between the highest and lowest observed HAQ Index was larger in 2015 than in 1990, ranging from 28·6 to 94·6. Of 195 geographies, 167 had statistically significant increases in HAQ Index levels since 1990, with South Korea, Turkey, Peru, China, and the Maldives recording among the largest gains by 2015. Performance on the HAQ Index and individual causes showed distinct patterns by region and level of development, yet substantial heterogeneities emerged for several causes, including cancers in highest-SDI countries; chronic kidney disease, diabetes, diarrhoeal diseases, and lower respiratory infections among middle-SDI countries; and measles and tetanus among...

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BACKGROUND: Postnatal care use is vital in saving mother and newborn lives which is a continuum of care for maternal, neonatal and child health. This reviewaimed to determine the utilization and determinants of postnatal care use in Ethiopia.METHODS: PubMed, Scopus, Web of Science, and Embase databases were searched on June 25, 2017. The study screening, data extraction and quality assessment were done independently by two reviewers. Effect sizes were pooled using a random-effectsmodel.RESULTS: Nine articles were included in the review. The pooled estimate for utilization of the service was 32% (95% CI: 21%, 43%). The pooled results of determinants of postnatal care use was statistically significant among those mothers who had ability to make decisions (1.89; 1.25, 2.54), had a history of antenatal care utilization (2.55; 1.42, 3.68), received more than two antenatal care visits (1.84; 1.28, 2.40), and received the service from skilled service provider (3.16; 1.62, 4.70). It was also found that mothers who gave birth in health faciliteis (2.13; 1.14, 3.12), had middle monthly income, richer, were from urban areas, and had knowledge of obstetric danger signs were significantly associated with increased odds of postnatal care use.CONCLUSION: Utilization of the services is low in Ethiopia. Antenatal care utilization, skilled service provider, being from urban area and delivery in health facility had a significant effect on postnatal care utilization. More rigorous studies are needed to identify determinant with the causal association to postnatal care utilization. The review was registered on PROSPEROCRD42017060266.

Aim Obesity is an emerging public health problem, with its incidence on the rise. An abnormal metabolic profile is a risk factor for developing obesity. Dietary factors play a central role in the regulation of inflammation and obesity. The aim of the present study was to determine the prevalence of metabolically healthy obese and metabolically unhealthy obese (MUO) phenotypes, and their association with dietary inflammatory index (DII) among obese Iranian people. Methods A cross‐sectional study was conducted from July to October 2017 among 300 obese participants in southern Tehran. DII scores were computed based on the overall inflammatory properties of 32 dietary components using dietary intake assessed by food frequency questionnaire. MUO phenotype was defined as having three or more of these metabolic abnormalities: high blood glucose, high triglycerides, low high‐density lipoprotein cholesterol, elevated blood pressure or abdominal obesity. The association was determined using logistic regression analysis. Results The MUO phenotype (n = 176) was found in 63.5% of obese participants. Compared with participants in the first quartile, those in the fourth quartile of DII score (more pro‐inflammatory diet) had higher odds of MUO phenotype (odds ratio, OR: 2.58 (95% CI: 1.19–5.59), P = 0.04), and there was a significant association between the continuous form of DII score and the odds of MUO phenotype (OR: 1.18 (95% CI: 1.01–1.37)) after adjusting for potential confounders. Conclusions Higher DII scores were positively associated with the MUO phenotype. A more pro‐inflammatory diet is a potential risk factor for MUO phenotype.