BackgroundNon-fatal outcomes of disease and injury increasingly detract from the ability of the world's population to live in full health, a trend largely attributable to an epidemiological transition in many countries from causes affecting children, to non-communicable diseases (NCDs) more common in adults. For the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015), we estimated the incidence, prevalence, and years lived with disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015.MethodsWe estimated incidence and prevalence by age, sex, cause, year, and geography with a wide range of updated and standardised analytical procedures. Improvements from GBD 2013 included the addition of new data sources, updates to literature reviews for 85 causes, and the identification and inclusion of additional studies published up to November, 2015, to expand the database used for estimation of non-fatal outcomes to 60 900 unique data sources. Prevalence and incidence by cause and sequelae were determined with DisMod-MR 2.1, an improved version of the DisMod-MR Bayesian meta-regression tool first developed for GBD 2010 and GBD 2013. For some causes, we used alternative modelling strategies where the complexity of the disease was not suited to DisMod-MR 2.1 or where incidence and prevalence needed to be determined from other data. For GBD 2015 we created a summary indicator that combines measures of income per capita, educational attainment, and fertility (the Socio-demographic Index [SDI]) and used it to compare observed patterns of health loss to the expected pattern for countries or locations with similar SDI scores.FindingsWe generated 9·3 billion estimates from the various combinations of prevalence, incidence, and YLDs for causes, sequelae, and impairments by age, sex, geography, and year. In 2015, two causes had acute incidences in excess of 1 billion: upper respiratory infections (17·2 billion, 95% uncertainty interval [UI] 15·4–19·2 billion) and diarrhoeal diseases (2·39 billion, 2·30–2·50 billion). Eight causes of chronic disease and injury each affected more than 10% of the world's population in 2015: permanent caries, tension-type headache, iron-deficiency anaemia, age-related and other hearing loss, migraine, genital herpes, refraction and accommodation disorders, and ascariasis. The impairment that affected the greatest number of people in 2015 was anaemia, with 2·36 billion (2·35–2·37 billion) individuals affected. The second and third leading impairments by number of individuals affected were hearing loss and vision loss, respectively. Between 2005 and 2015, there was little change in the leading causes of years lived with disability (YLDs) on a global basis. NCDs accounted for 18 of the leading 20 causes of age-standardised YLDs on a global scale. Where rates were decreasing, the rate of decrease for YLDs was slower than that of years of life lost (YLLs) for nearly every cause included in our analysis. For low SDI geographies, Grou...

Multimorbidity was associated with an increase in risk of death. Multimorbidity measurement standardization is needed to produce more comparable estimates. Adjusted analysis which includes potential confounders might contribute to better understanding of causal relationships between multimorbidity and mortality.

The aim of this study was to describe quality indicators for prenatal care in Brazil as part of the Program for the Improvement of Access and Quality (PMAQ-AB). The study analyzed number of prenatal visits, vaccination status, prescription of ferrous sulfate, physical examination, orientation, and laboratory tests, based on which a summary quality indicator was constructed. Data were collected in 2012-2013 during interviews conducted by External Evaluators of the PMAQ-AB, with 6,125 users who had done their last prenatal follow-up in Family Health units. During prenatal follow-up, 89% reported six or more visits, more than 95% received a tetanus booster and prescription of ferrous sulfate, 24% reported having received all the procedures in the physical examination, 60% received all the orientation, and 69% had all the recommended laboratory tests. Only 15% of interviewees had received adequate prenatal care, including all the recommended measures, and there was a significantly higher proportion of "complete" care in pregnant women that were older, with higher income, in the Southeast region of Brazil, in municipalities with more than 300,000 inhabitants, and in those with (HDI) in the upper quartile. There are persist social and individual inequalities that can be targeted by measures to upgrade the teams' work processes.

SummaryBackgroundPolitical, economic, and epidemiological changes in Brazil have affected health and the health system. We used the Global Burden of Disease Study 2016 (GBD 2016) results to understand changing health patterns and inform policy responses.MethodsWe analysed GBD 2016 estimates for life expectancy at birth (LE), healthy life expectancy (HALE), all-cause and cause-specific mortality, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs), and risk factors for Brazil, its 26 states, and the Federal District from 1990 to 2016, and compared these with national estimates for ten comparator countries.FindingsNationally, LE increased from 68·4 years (95% uncertainty interval [UI] 68·0–68·9) in 1990 to 75·2 years (74·7–75·7) in 2016, and HALE increased from 59·8 years (57·1–62·1) to 65·5 years (62·5–68·0). All-cause age-standardised mortality rates decreased by 34·0% (33·4–34·5), while all-cause age-standardised DALY rates decreased by 30·2% (27·7–32·8); the magnitude of declines varied among states. In 2016, ischaemic heart disease was the leading cause of age-standardised YLLs, followed by interpersonal violence. Low back and neck pain, sense organ diseases, and skin diseases were the main causes of YLDs in 1990 and 2016. Leading risk factors contributing to DALYs in 2016 were alcohol and drug use, high blood pressure, and high body-mass index.InterpretationHealth improved from 1990 to 2016, but improvements and disease burden varied between states. An epidemiological transition towards non-communicable diseases and related risks occurred nationally, but later in some states, while interpersonal violence grew as a health concern. Policy makers can use these results to address health disparities.FundingBill & Melinda Gates Foundation and the Brazilian Ministry of Health.

Interest in multimorbidity -commonly defined as the co-occurrence of at least two chronic conditions in the same individual 1 -has increased in the past few years owing to its substantial effect on the individual and the individual's family, as well as on health systems and on society, particularly in resource-poor settings 2-4 . Multimorbidity is distinct from the related concept of comorbidity, which refers to the combined effects of additional conditions in relation to the index condition in an individual [5][6][7][8] . By contrast, care for multimorbidity is patient-centred and does not routinely give priority to any single condition, although in clinical care, patients and clinicians will usually focus on the most pressing problems that the patient is experiencing.People with multimorbidity are more likely to die prematurely, be admitted to hospital and have an increased length of stay than people with a single chronic condition 9,10 . Multimorbidity is also associated with poorer function and health-related quality of life (HRQOL), depression and intake of multiple drugs (polypharmacy) and greater socioeconomic costs [11][12][13][14][15][16][17][18] . Most health care is designed to treat individual conditions rather than providing comprehensive, person-centred care 2,19,20 , which often leads to fragmented and sometimes contradictory care for people with multimorbidity and increases their treatment burden 21 Moreover, treating one condition at a time is inefficient and unsatisfactory for both people with multimorbidity and their health-care providers [22][23][24] .Multimorbidity is increasingly common owing to changes in lifestyle risk factors, notably physical inactivity and obesity, and population ageing that in part reflects improvements in survival from acute and chronic conditions 2,19,25,26 . Multimorbidity is associated with socioeconomic status and age 3,19,25,27 . However, although age is the strongest driver of multimorbidity, in absolute numbers, more people <65 years of age have multimorbidity than people ≥65 years of age, partly because more people in the general population are in that age group. Moreover, this emphasizes that multimorbidity is not just a feature of ageing 19,26 .Multimorbidity is further complicated in low-income and middle-income countries (LMICs) by the overlap of compounding factors, including adverse environmental and early life stressors linked to poverty, limited social infrastructure and poorer family coping mechanisms, that translate into chronic diseases occurring at earlier ages [28][29][30][31] . LMICs also have a higher prevalence of multimorbidity-related financial burden 32,33 and have weaknesses in health systems including a greater focus Treatment burdenThe workload associated with managing treatments and health-care recommendations and the impact of this on an individual and their supporters.

No abstract

a high proportion of elderly presented functional disability; the outcomes were associated to the following variables: demographic, socioeconomic, behavioral, health status and use of health services.

BackgroundThe body of evidence on associations between socioeconomic status (SES) and sedentary behaviors in adolescents is growing.ObjectivesThe overall aims of our study were to conduct a systematic review and meta-analysis of this evidence and to assess whether (1) the associations between SES and sedentary behavior are consistent in adolescents from low-middle-income and from high-income countries, (2) the associations vary by domain of sedentary behavior, and (3) the associations vary by SES measure.MethodsWe performed a systematic literature search to identify population-based studies that investigated the association between SES and sedentary behavior in adolescents (aged 10–19 years). Only studies that presented risk estimates were included. We conducted meta-analyses using random effects and univariate meta-regression and calculated pooled effect sizes (ES).ResultsData from 39 studies were included; this provided 106 independent estimates for meta-analyses. Overall, there was an inverse association between SES and sedentary behavior (ES 0.89; 95 % confidence interval [CI] 0.81–0.98). However, the direction of the association varied: in high-income countries, SES was inversely associated with sedentary behavior (ES 0.67; 95 % CI 0.62–0.73), whereas in low-middle-income countries, there was a positive association between SES and sedentary behavior (ES 1.18; 95 % CI 1.04–1.34). In high-income countries, the associations were strongest for screen time (ES 0.68; 95 % CI 0.62–0.74) and television (TV) time (ES 0.58; 95 % CI 0.49–0.69), whereas in low-middle-income countries, the associations were strongest for ‘other’ screen time (i.e., computer, video, study time, but not including TV time) (ES 1.38; 95 % CI 1.07–1.79). All indicators of SES were negatively associated with sedentary behavior in high-income countries, but only resources (income and assets indexes) showed a significant positive association in low-middle-income countries.ConclusionThe associations between SES and sedentary behavior are different in high- and low-middle-income countries, and vary by domain of sedentary behavior. These findings suggest that different approaches may be required when developing intervention strategies for reducing sedentary behavior in adolescents in different parts of the world.Electronic supplementary materialThe online version of this article (doi:10.1007/s40279-016-0555-4) contains supplementary material, which is available to authorized users.