Global Burden of Disease Cancer Collaboration IMPORTANCE Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. OBJECTIVE To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. EVIDENCE REVIEW We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. FINDINGS In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572 000 deaths and 15.2 million DALYs), and stomach cancer (542 000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819 000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601 000 deaths and 17.4 million DALYs), TBL cancer (596 000 deaths and 12.6 million DALYs), and colorectal cancer (414 000 deaths and 8.3 million DALYs). CONCLUSIONS AND RELEVANCE The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equ...

Background Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019.Methods 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10-14 and 50-54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed agespecific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. FindingsThe global TFR decreased from 2•72 (95% uncertainty interval [UI] 2•66-2•79) in 2000 to 2•31 (2•17-2•46) in 2019. Global annual livebirths increased from 134•5 million (131•5-137•8) in 2000 to a peak of 139•6 million (133•0-146•9) in 2016. Global livebirths then declined to 135•3 million (127•2-144•1) in 2019. Of the 204 countries and territories included in...

PurposeDyslipidemia has been established as one of the most important modifiable risk factors for cardiovascular disease. Due to the higher prevalence of dyslipidemia in males, this study aimed to estimate the prevalence of dyslipidemia in Iranian urban men.Materials and MethodsA screening program was conducted in 845 Iranian men 25 years of age and older in 2014. A health interview survey was conducted to evaluate the prevalence of self-reported dyslipidemia and to collect demographic data, as well as serum lipid profile screening by a reference laboratory. Lipoprotein levels was categorized based on the Adult Treatment Panel III criteria and the data were analyzed using the chi-square test and analysis of variance.ResultsThe overall prevalence of dyslipidemia was 51.8%, and the prevalence of various forms of dyslipidemia was as follows: hypercholesterolemia (≥240 mg/dL), 11.4%; hyper-low-density lipoprotein cholesterol (≥160 mg/dL), 9.6%; hypertriglyceridemia (≥200 mg/dL), 25%; and hypo-high-density lipoprotein (HDL) cholesterol (<40 mg/dL), 34.3%. With the exception of hypo-HDL, all forms of dyslipidemia were significantly less common in men over 65 years of age (p<0.05).ConclusionsThe prevalence of hypo-HDL and hypertriglyceridemia was higher than expected in Iranian adult men, with half of men 25 years of age and older affected by at least one form of dyslipidemia. A large gap in primary and secondary care was observed, because nearly 80% of patients with dyslipidemia were unaware of their status. Urgent preventive programs and lifestyle changes are necessary to reduce the prevalence of cardiovascular risk factors.

BACKGROUND:Social support is one of the most effective factors on the diabetic self-care. This study aimed to assess social support and its relationship to self-care in type 2 diabetic patients in Qom, Iran.STUDY DESIGN:A cross-sectional study was conducted on 325 diabetics attending the Diabetes Mellitus Association.METHODS:Patients who meet inclusion and exclusion criteria were selected using random sampling method. Data were collected by the Summary of Diabetes Self-Care Activities and Multidimensional Scale of Perceived Social Support, with hemoglobin A1C test. Data were analyzed using descriptive statistics and independent t-test, analysis of variance, Pearson correlation, and linear regression test, using 0.05 as the critical significance level, provided by SPSS software.RESULTS:The mean and standard deviation of self-care and social support scores were 4.31 ± 2.7 and 50.32 ± 11.09, respectively. The mean level of glycosylated hemoglobin (HbA1C) of patients was 7.54. There was a significant difference between mean score of self-care behaviors and social support according to gender and marital status (P < 0.05). The regression analysis showed that disease duration was the only variable which had a significant effect on the level of HbA1C (P < 0.001). Pearson correlation coefficient indicated that self-care and social support significantly correlated (r = 0.489, P > 0.001) and also predictive power of social support was 0.28. Self-care was significantly better in diabetics with HbA1C ≤7%. Patients who had higher HbA1C felt less, but not significant, social support.CONCLUSIONS:This study indicated the relationship between social support and self-care behaviors in type 2 diabetic patients. Interventions that focus on improving the social support and self-care of diabetic control may be more effective in improving glycemic control.

Background: Although some healthcare reforms such as Health Transformation Plan (HTP) were implemented in Iran to provide required healthcare services, few studies have been conducted to track the impacts of these reforms on socioeconomic inequality in healthcare utilization. This study aims to track socioeconomic inequalities in healthcare utilization and their changes between 2008 and 2016 in Iran. Methods: Required data were obtained from two of Iran's utilization of healthcare services survey conducted in 2008 and 2016. Erreygers concentration index (EI) was used to measure inequality in the utilization of outpatient and inpatient healthcare services (UOH and UIH). The decomposition of EI (DEI) was used to explain healthcare utilization inequality. Oaxaca decomposition (OD) was also employed to track the changes in EI in this period. Result: Inequality in UOH increased from 0.105 to 0.133 in the studied years, indicating the pro-rich distribution of UOH. Inequality in UIH decreased from 0.0558 to − 0.006. DEI showed that economic status was the main factor that contributed to inequality in the UOH and UIH. OD showed that residence in rural areas and supplementary insurance were the main contributing factors in the increased inequality of UOH. Moreover, OD also showed that economic status was the main contributing factor in the reduced inequality of UIH. Conclusion: While Iran still suffers from significant socioeconomic inequalities in UOH, it seems that healthcare reforms, especially HTP, have reduced UIH inequality. Expanding healthcare reforms into the outpatient sector and also implementing effective health financing policies could be recommended as a remedy against UOH inequality.