Biochemical Profiles of Pregnant and Non-pregnant Women Attending at the University of Gondar Hospital, Northwest Ethiopia: A Comparative Cross-sectional Study

BACKGROUND: Pregnancy is a natural physiological statement with hormonal and metabolic changes that helps the growth and survival of the fetus. However, biochemical profiles derangement may lead to pregnancy complications. Therefore, there is a need for determining biochemical profiles among pregnant women.METHODS: A comparative cross-sectional study was conducted among pregnant and non-pregnant women at the University of Gondar Hospital, from February to April, 2015. Fasting blood sample was collected from 139 pregnant and 139 age matched non-pregnant women using systematic random sampling technique. Interviewer-administered questionnaire was used to collect socio-demographic and clinical data. Fasting blood glucose and lipid profile were measured by A25 Biosytemchemistry analyzer using enzymatic calorimetric methods. Data analysis was done using SPSS version 20. Level of significance between groups was analyzed using independent student t-test and Mann-Whitney U test. A p-value of <0.05 was considered as statistically significant.RESULT: Pregnant women as compared to non-pregnant had significantly increased glucose (96.35+14.45 and 81.12+9.86 mg/dl), total cholesterol (211.9+40.88 and 172.40+29.64 mg/dl) [p<0.05], respectively. It had also significantly high triglycerides (190.81+81.04 and 107.43+45.80 mg/dl) and low-density lipoprotein cholesterol (116.03+37.26 and 86.12+27.29mg/dl) [p<05] in pregnant as compared to non-pregnant women. The level of high-density lipoprotein cholesterol was significantly lower in pregnant women (59.58+14.26) than control (63.63+11.4, P <0.05).CONCLUSION: There were statistically significant increment in glucose, total cholesterol, triglycerides, low-density lipoprotein cholesterol and decrement in high-density lipoprote in cholesterol levels among pregnant women compared with non-pregnant women. Therefore, pregnant women have to be monitored closely for their biochemical profiles to avoid adverse pregnancy outcomes.KEYWORDS: Pregnancy, biochemical profiles, Gondar, Ethiopi

Correlation between serum lipid profile with anthropometric and clinical variables in patients with type 2 diabetes mellitus

Background: The problem of dyslipidemia is high in patients with diabetes mellitus. There is ample evidence that abnormalities in lipid metabolism are important risk factors for increased incidence of diabetes associated complications. The most important risk indicators for these complications are lipid profile abnormalities. Therefore, the aim of this study was to assess the correlation between serum lipid profile with anthropometric and clinical variables among type 2 diabetes mellitus patients.Methods: A comparative cross sectional study was conducted at University of Gondar Hospital from February to April in 2015. A total of 296 participants (148 case and 148 healthy controls) were selected using systematic random sampling technique. Socio- demographic characteristics and clinical data were collected using pretested structured questionnaire incorporating the WHO Stepwise approach. Fasting venous blood sample was collected for blood sugar; lipid profile investigations and the blood levels were determined by Bio Systems A25 Chemistry Analyzer (Costa Brava, Spain). Independent sample t-test and Man Whitney U test were used to compare means. P-value < 0.05 was considered statistically significant.Results: Overall, T2DM patients had significantly higher total cholesterol ([205.4±50.9vs184.9±44.1]mg/dl), low density lipoprotein ([113.1±43.2vs100.1±36.4] mg/dl) and triacylglycerol ([189.22± 100.9 vs 115.13±59.2] mg/dl), and significant decline of high density lipoprotein cholesterol ([56.5±20.4vs62.1±13] mg/dl) as compared to healthy controls, respectively. Triacylglycerolemia was significantly associated with the risk of cardiovascular disease (AOR: 1.015; 95%CI: 1.010-1.021). Evident correlation was observed between anthropometric and clinical variables with lipid profile.Conclusion: Higher serum levels of fasting blood sugar, total cholesterol, low density lipoprotein cholesterol, and triacylglycerol and lower levels of high density lipoprotein cholesterol are found in type 2 diabetes mellitus patients. Thus, DM patients are more prone to dyslipidemia which is an important risk factor for atherosclerosis and coronary heart disease.Keywords: Type 2 diabetes mellitus, lipid profile, Ethiopi

Chronic Kidney Disease and Associated Risk Factors Assessment among Diabetes Mellitus Patients at A Tertiary Hospital, Northwest Ethiopia

BACKGROUND: The prevalence of chronic kidney disease, particularly in diabetic patients, is increasing rapidly throughout the world. Nowadays, many individuals in developing nations are suffering from diabetes which is one of the primary risk factors of chronic kidney disease.METHODS: Institution based cross-sectional study was conducted at the University of Gondar Hospital from February to April 2016. A total of 229 study participants were selected using systematic random sampling technique. Urine sample was collected for albumin determination by dipstick. The Simplified Modification of Diet in Renal Disease study equation was used to estimate glomerular filtration rate. Binary logistic regression model was used to identify risk factors.RESULTS: Of the total 229 study participants, 50.2% were females and the mean age was 47±15.7 years. Among study participants, the prevalence of chronic kidney disease (CKD) was found to be 21.8% (95% CI: 16% - 27%). Of all study participants, 9(3.9%) had renal impairment (eGFR < 60 ml/min/ 1.73 m2) and 46 (20.1%) had albuminuria. Older age (AOR: 5.239, 95% CI: 2.255-12.175), systolic blood pressure ≥140mmHg (AOR: 3.633, 95% CI: 1.597-8.265), type 2 diabetes mellitus (AOR: 3.751, 95% CI: 1.507-9.336) and longer duration of diabetes (AOR: 3.380, 95% CI: 1.393-8.197) were independent risk factors of CKD.CONCLUSIONS: The study identified high prevalence (21.8%) of CKD among diabetic adults. CKD was significantly associated with older age, systolic blood pressure, type 2 DM and longer duration of DM. Thus, DM patients should be diagnosed for chronic kidney disease and then managed accordingly.

Intestinal Parasitosis and Their Associated Factors among People Living with HIV at University of Gondar Hospital, Northwest-Ethiopia

BACKGROUND: Most HIV clients die of AIDS related intestinal parasitic infections rather than due to the HIV infection itself. Therefore, this study was aimed at determining the prevalence of intestinal parasite and their associated factors among HIV/AIDS clients at the University of Gondar Hospital, Northwest Ethiopia.METHODS: Institution based cross sectional study was conducted using systematic random sampling technique from March to May 2016. A semi-structured questionnaire was used to collect data. Stool samples were collected and processed using direct wet mount, formol-ether concentration and modified Ziehl-Neelson staining techniques. Besides, blood samples were collected for CD4+ count estimation. Both descriptive and logistic regression analyses were used in data analysis. P-values <0.05 were considered as statistically significant.RESULTS: A total of 223 participants were enrolled in this study, and the prevalence of intestinal parasitosis was found to be 29.1%. The most predominant intestinal parasite detected was cyst of Entamoeba histolytica (8.5%) followed by Ascaris lumbricoides (6.7%), Strongyloides sterocoralis (3.6%) and Cryptosporidium parvum (3.1%), whereas Schistosoma mansoni (0.9%) and Hymenolepis nana (0.9%) were the least detected. Absence of toilet (AOR= 19.4, CI: 6.46-58.3), improper hand washing before meal (AOR=11.23, 95% CI: 4.16-30.27 and CD4+ count < 200 cells/mm3 (AOR=33.31, 95% CI: 9.159-121.149) had significant association with prevalence of intestinal parasites.CONCLUSION: The study indicated that intestinal parasites are still a problem among HIV/AIDS patients in the study area. Thus, routine examination for intestinal parasites and interventions should be carried out for better management of clients.KEYWORDS: Intestinal Parasites, HIV/AIDS, associated factors, Gondar, Ethiopi

Errors in the total testing process in the clinical chemistry laboratory at the University of Gondar Hospital, northwest Ethiopia

Background: Laboratory services have been described as the major processes contributing to safe patient care in the modern healthcare sector. However, occurrences of errors in the overall testing processes impair the clinical decision-making process. Such errors are supposed to be high in resource-poor countries, like Ethiopia. The objective of this study was to assess errors in the total testing process in the Clinical Chemistry laboratory of the University of Gondar Hospital, Northwest EthiopiaMethods: A cross-sectional study was conducted at the University of Gondar Hospital from February to March 2016. All the required data were collected using established quality indicators. Data were analyzed using SPSS version 20. Frequencies and cross-tabulations were used to summarize descriptive statistics.Results: A total of 3259 samples and corresponding laboratory request forms were received for analysis. The analysis of the overall distribution of errors revealed that 89.6% were preanalytical errors, 2.6% were analytical, and 7.7% were postanalytical errors. Of the pre-analytical errors, incomplete request form filling was the most frequent error observed, followed by sample rejection rate (3.8%). Analytical errors related to internal and external quality control exceeding the target range, (14.4%) and (51.4%) respectively, were reported. Excessive turnaround time and unreported critical value cases were the major defects in the post-analytical phase of quality assurance.Conclusion: The present finding showed relatively high frequency of errors, which alarms the importance of quality indicators to assess errors in the total testing process. The University of Gondar Hospital laboratory should improve the quality of healthcare services based on these findings using laboratory standards.Keywords: Analytical errors; clinical laboratory; postanalytical errors; pre-analytical errors; qualit

Undiagnosed diabetes mellitus and associated factors among psychiatric patients receiving antipsychotic drugs at the University of Gondar Hospital, northwest Ethiopia

Background: Undiagnosed diabetes mellitus cases are at higher risk for diabetic related complications. In low-income African countries, patients with undiagnosed diabetes mellitus account for 75% of diabetes cases. Psychiatric disorders have a greater impact on the global burden of diseases and disability associated with chronic diseases like diabetes mellitus and cardiovascular diseases.Methods: Institution based cross-sectional study was conducted at the University of Gondar Hospital from February to April 2016. A total of 205 psychiatric patients aged above 15 years that were taking antipsychotic were included by the simple random sampling method. Fasting blood glucose, triglycerides and cholesterol level were determined from venous blood samples to evaluate diabetes mellitus based on WHO criteria.Results: Among 205 psychiatric patients taking antipsychotics, 15(7.3%) had undiagnosed diabetes mellitus. Duration of antipsychotic treatment and sex had a statistically significant association with the prevalence of undiagnosed diabetes mellitus. As the duration of antipsychotic drug treatment increased by one year, the risk of having a diabetes mellitus increase by 1.47 times (AOR: 1.47 CI: 1.021-2.125).Conclusion: The prevalence of undiagnosed diabetes mellitus among psychiatry patients taking antipsychotics was higher than the estimated diabetes national prevalence of Ethiopia. Screening of diabetes mellitus in particular, patients having a longer duration of antipsychotic treatment is mandatory to bring more undiagnosed cases for medical attention.Keywords: Diabetes mellitus, Psychiatric disorder, Antipsychoti

The Prevalence of Metabolic Syndrome and Its Components among Type 2 Diabetes Mellitus Patients at a Tertiary Hospital, Northwest Ethiopia

BACKGROUND: Metabolic syndrome is a cluster of risk factors that is responsible for the risk of coronary heart disease and stroke. Therefore, the aim of this study was to assess the prevalence of MetS and its components among T2DM patients.METHODS: A cross-sectional study was conducted at the Diabetes Clinic of the Hospital, from June to July, 2015. Data were entered into EPI INFO software and exported to SPSS 20 for analysis. MetS prevalence was estimated using NCEP ATPIII and IDF criteria. Anthropometric measurements, investigations of serum glucose and lipid profiles were done. Logistic regression analysis was used to evaluate associated factors. A P-value ≤ 0.05 wasconsidered statistically significant.RESULT: A total of 159 participants were included in the study; 119 (59.7%) were females with mean (±SD) age of (49.8±8.7) year. The prevalence of MetS was 66.7% in NCEP-ATP III and 53.5% in IDF definitions. The most prevalent component of MetS was elevated triglyceride (56.6% in ATPIII and 62.3% in IDF criteria), followed by abdominal obesity (61%) IDF and elevated blood pressure (55.4%) NCEP-ATPIII criteria. The regression analysis showed that increased age, being female, high BMI, having diabetes for over 5 years and poor glycemic control were significantly associated with metabolic syndrome.CONCLUSION: The prevalence of MetS and its components among T2DM patients were high, suggesting that diabetic patients are at increased risk of CVD and other complications. Efforts should be geared towards addressing these abnormalities through lifestyle modification, health awareness and medications in order to reduce this complication.

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories.Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator.Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator

Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. Methods The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODErn), to generate cause fractions and cause specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. Findings At the broadest grouping of causes of death (Level 1), non-communicable diseases (NC Ds) comprised the greatest fraction of deaths, contributing to 73.4% (95% uncertainty interval [UI] 72.5-74.1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 186% (17.9-19.6), and injuries 8.0% (7.7-8.2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22.7% (21.5-23.9), representing an additional 7.61 million (7. 20-8.01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7.9% (7.08.8). The number of deaths for CMNN causes decreased by 222% (20.0-24.0) and the death rate by 31.8% (30.1-33.3). Total deaths from injuries increased by 2.3% (0-5-4-0) between 2007 and 2017, and the death rate from injuries decreased by 13.7% (12.2-15.1) to 57.9 deaths (55.9-59.2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000-289 000) globally in 2007 to 352 000 (334 000-363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118.0% (88.8-148.6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36.4% (32.2-40.6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33.6% (31.2-36.1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respirator}, infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990 neonatal disorders, lower respiratory infections, and diarrhoeal diseases were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. Interpretation Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.Peer reviewe