Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries.Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution.Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990-2010 time period, with the greatest annualised rate of decline occurring in the 0-9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10-24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the...

Background Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. MethodsGBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk-outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk-outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk-outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each agesex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobac...

Many countries worldwide are aging rapidly, and the complex care needs of older adults generate an unprecedented demand for health services. Common reasons for elderly emergency department (ED) visits frequently involve conditions triggered by preventable infections also known as ambulatory care sensitive conditions (ACSCs). This study aims to describe the trend and the associated disease burden attributable to ACSC-related ED visits made by elderly patients and to characterize their ED use by nursing home residence. We designed a population-based ecological study using administrative data on Taiwan EDs between 2002 and 2013. A total of 563,647 ED visits from individuals aged 65 or over were examined. All elderly ED visits due to ACSCs (tuberculosis, upper respiratory infection, pneumonia, sepsis, cellulitis and urinary tract infection (UTI)) were further identified. Subsequent hospital admissions, related deaths after discharge, total health care costs and disability-adjusted life years (DALYs) were compared among different ACSCs. Prevalence of ACSCs was then assessed between nursing home (NH) residents and non-NH residents. Within the 12-year observation period, we find that there was a steady increase in both the rate of ACSC ED visits and the proportion of elderly with a visit. Overall, pneumonia is the most prevalent among six ACSCs for elderly ED visits (2.10%; 2.06 to 2.14), subsequent hospital admissions (5.77%; 5.59 to 5.94) and associated mortality following admission (17.37%; 16.74 to 18.01). UTI is the second prevalent ACSC consistently across ED visits (2.02%; 1.98 to 2.05), subsequent hospital admissions (2.36%, 2.25 to 2.48) and mortality following admission (10.80%; 10.28 to 11.32). Sepsis ranks third highest in the proportion of hospitalization following ED visit (2.29%; 2.18 to 2.41) and related deaths after hospital discharge (7.39%; 6.95 to 7.83), but it accounts for the highest average total health care expenditure (NT$94,595 ± 120,239; ≈US$3185.02) per case. When examining the likelihood of ACSC-attributable ED use, significantly higher odds were observed in NH residents as compared with non-NH residents for: pneumonia (adjusted odds ratio (aOR): 5.01, 95% confidence interval (CI) 4.50–5.58); UTI (aOR: 4.44, 95% CI 3.97–4.98); sepsis (aOR: 3.54, 95% CI 3.06–4.10); and tuberculosis (aOR: 2.44, 95% CI 1.63–3.65). Here we examined the ACSC-related ED care and found that, among the six ACSCs studied, pneumonia, UTI and sepsis were the leading causes of ED visits, subsequent hospital admissions, related mortality, health care costs and DALYs in Taiwanese NH elderly adults. Our findings suggest that efficient monitoring and reinforcing of quality of care in the residential and community setting might substantially reduce the number of preventable elderly ED visits and alleviate strain on the health care system.

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Supplement Table 1. Classification of infection sites (sepsis origins) by ICD-9-CM codes. Diagnostic category Codes Description 1. Central nervous system infection 320 Bacterial meningitis 322 Meningitis, unspecified 324 Central nervous system abscess 325 Phlebitis of intracranial sinus 2. Respiratory system infection

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The aim of the study was to investigate the long-term association between contrast medium exposure during computed tomography (CT) and the subsequent development of end-stage renal disease (ESRD) in patients with chronic kidney disease (CKD).We conducted a population-based cohort study using Taiwan's National Health Insurance Research Database. A total of 7100 patients with nonadvanced CKD who underwent contrast medium-enhanced CT were identified and served as the study cohort. To avoid selection bias, we used the propensity score to match 7100 nonadvanced CKD patients, who underwent noncontrast medium-enhanced CT to serve as the comparison cohort. The age, sex, index year, and frequency of undergoing CTs were also matched between the study and comparison cohorts. Participants were followed until a new diagnosis of ESRD or December 31, 2011. Hazard ratios (HRs) with 95% confidence interval (95% CI) were calculated using the Cox proportional hazards regression.Contrast medium exposure was not identified as a risk factor for developing ESRD in nonadvanced CKD patients after confounders adjustment (adjusted HR = 0.91; 95% CI, 0.66–1.26; P = 0.580). We further divided the patients who underwent CTs with contrast medium use into ≤1 exposure per year on average, >1 and <2 exposure per year on average, and ≥2 exposure per year on average. After adjusting for confounders, we identified a much higher risk for developing ESRD in the 2 groups of >1 and <2 exposure per year on average and ≥2 exposure per year on average (adjusted HR = 8.13; 95% CI, 5.57–11.87 and adjusted HR = 12.08; 95% CI, 7.39–19.75, respectively) compared with the patients who underwent CTs without contrast medium use.This long-term follow-up study demonstrated that contrast medium exposure was not associated with an increased risk of ESRD development in nonadvanced CKD patients.

Objective: Lung cancer is not only one of the most prevalent cancers but is also a lethal disease with a very low 5-year survival rate. Delay in diagnosis further reduces the chance of early treatment and worsens patients' survival. The purpose of this study was to understand the delay in the diagnosis of lung cancer under the healthcare system in Taiwan, and to identify the factors associated with it. Methods: A total of 840 patients diagnosed with lung cancer who had completed or were undergoing cancer treatments were recruited from a medical center in central Taiwan from July 2007 to January 2011. Structured questionnaires were administered regarding demographic characteristics, factors associated with their time to diagnosis and the length of delay in days. Results: Mean age was 62.68 years with 52.16 days to diagnosis on average. Number of hospital visits before confirmation of diagnosis differed significantly with the level of healthcare institution initially visited (P , 0.001). Compared with patients who had three or more hospital visits, patients who only visited two and one hospital(s) had a significant 34.91-day (95% confidence interval: 16.29 -53.53) and 42.25-day (95% confidence interval: 20.76 -63.76) reduction in their time to diagnosis (P , 0.001). Conclusions: As the number of hospital visits increased, the delay in diagnosis also increased. It is vital to shorten the time to diagnosis for lung cancer patients by limiting the number of medical visits and educating the public to restrict excessive use of medical resources and strengthen their trust in medical professionals.