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

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.

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

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.

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

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

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. Findings: From 1950 to 2017, TFRs decreased by 49\ub74% (95% uncertainty interval [UI] 46\ub74–52\ub70). The TFR decreased from 4\ub77 livebirths (4\ub75–4\ub79) to 2\ub74 livebirths (2\ub72–2\ub75), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83\ub78 million people per year since 1985. The global population increased by 197\ub72% (193\ub73–200\ub78) since 1950, from 2\ub76 billion (2\ub75–2\ub76) to 7\ub76 billion (7\ub74–7\ub79) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2\ub70%; this rate then remained nearly constant until 1970 and then decreased to 1\ub71% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2\ub75% in 1963 to 0\ub77% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2\ub77%. The global average age increased from 26\ub76 years in 1950 to 32\ub71 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59\ub79% to 65\ub73%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1\ub70 livebirths (95% UI 0\ub79–1\ub72) in Cyprus to a high of 7\ub71 livebirths (6\ub78–7\ub74) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0\ub708 livebirths (0\ub707–0\ub709) in South Korea to 2\ub74 livebirths (2\ub72–2\ub76) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0\ub73 livebirths (0\ub73–0\ub74) in Puerto Rico to a high of 3\ub71 livebirths (3\ub70–3\ub72) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2\ub70% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Funding: Bill & Melinda Gates Foundation