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Question: response qu estions part a to b a b include...

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Response Qu estions Part A To B A. B. Include the major problem of this article Include the specific question you are trying to answer of this article Introduction Previous studies have reported on health disparities in US states and counties. These studies showed that health disparities have increased with time. Recent attention has focused on increased motality in some age groups and a decline in life expectancy. In addition, the performance of the US health care system does not match its level of spending on health and lags behind countries with similar financial resources. For example, in 2014. US life expectancy ranked 43rd in the world, although the United States spent the most (S3.0 trillion) on health care exceeding the median amount spent by Organisation for Economic Co-operation and Development countries by 35%. Several studies have shown large variations in risk factors by state and county, and these variations have contributed to differences in health outcomes. In the Global Burden of Disease Study 2010 (GBD 2010) US Burden of Disease report, the following risk factors were reported as the main causes associated with US morbidity and mortality (percent contributed to total disability-adjusted life-years [DALYs] in 2010): poor diet (14%), smoking 11%), high blood pressure (8%), and obesity (11%). None of the previous studies of US health have been as comprehensive as the GBD study. The GBD systematically accounts for differences in data sources and biases and analyzes levels and trends for causes and risk factors within the same computational framework, which maximizes comparability across states, years, and different age groups by sex. GBD is now conducted on an annual cycle, with GBD 2016 providing updated estimates of mortality, morbidity, and risk factors in 195 locations, including the United States, from 1990 to 2016 The findings of GBD 2016 indicate that while the United States overall is experiencing improvements in health outcomes, the patterns of health burden at the state level vary across geography. Routinely monitoring the trend of burden of disease at the state level is essential given the vital role of states in many aspects of health and social policy -from the Medicaid program to regulation of private insurers and considering that individual states also experience different economic circumstances. The current study uses GBD 2016 to report the change in burden of disease, including injuries and risk factors at the state level, from 1990 to 2016 Methods Overview The GBD study is estimated annually and each round of results is internally consistent (cause-specific mortality estimates are summed to match all-cause mortality estimates) and collectively exhaustive (residual categories [other] are captured to enable quantifying total burden). The numbers reported in the previous round of GBD are not identical to those of the current round (GBD 2016) for 2 main reasons. First, since the GBD 2010 Special Communication regarding US risk factors, there has been further refinement of the garbage coding (ie, ill-defined causes of death) redistribution methods. Second, the new analysis at the state level changes some of the estimation slightly when aggregated to the national level. GBD 2016 provides a new time series. The GBD 2016 methodology has been published previously. GBD uses several metrics to report results for health loss related to specific diseases, injuries, and risk factors: deaths and death rates, years of life lost (YLLs) due to premature mortality, prevalence and prevalence rates for sequelae, years lived with disability (YLDs), and DALYs. GBD provides a comprehensive assessment of all-cause mortality and estimates for death due to 264 causes in 195 countries and territories from 1990 to 2016, as well as 333 causes of DALYs. GBD 2016 has 4 levels of causes that are mutually exclusive. Level 1 has 3 causes: communicable, maternal, neonatal, and nutritional disorders; noncommunicable diseases; and injuries

Data To estimate the US burden of disease prevalence, computation for each sequela began with a systematic analysis of published studies and available data sources providing information on prevalence, incidence, remission, and excess mortality, such as the National Health and Nutrition Examination Surveys, state inpatient databases, the National Ambulatory Medical Care Survey, National Hospital Ambulatory Medical Care Survey, Medical Expenditure Panel Survey, National Comorbidity Survey. National Epidemiological Survey on Alcohol and Related Conditions, National Survey on Drug Use and Health, US Department of Agriculture Continuing Survey of Food Intakes, Marketscan, National Health Interview Survey, Behavioral Risk Factor Surveillance System, and the Centers for Disease Control and Prevention Disease Surveillance Reports. Hospital inpatient data were extracted and used for this analysis. Moreover, outpatient encounter data were available for the United States through aggregate data derived from a database of claims information for US private and public insurance schemes for the years 2000, 2010, and 2012. GBD methodology applied several correction factors to account for bias in health service encounter data from these claims that were available as aggregated by Intemational Classification of Diseases (ICD) code and by primary diagnosis only. First, for chronic disorders, the study estimated the ratio between prevalence from primary diagnoses and prevalence from all diagnoses associated with a claim. Second, the claims data were used to generate the mean number of outpatient visits per disorder Similarly, the study generated per-person discharge rates from hospital inpatient data in the United States. All-Cause Mortality and Cause of Death All-cause mortality was estimated by age, sex, geography, and year using 6 modeling approaches to assess cause specific mortality; the Cause of Death Ensemble Model was used to generate estimates for the vast majority of causes. This analysis used deidentified death records from the National Center for Health Statistics (NCHS) and population counts from the US Census Bureau, NCHS, and the Human Mortality Database. Deaths and population were tabulated by county, age group, sex, year, and (in the case of death data) cause. The cause list developed for the GBD is arranged hierarchically in 4 levels. Within each level, the cause list is designed such that all deaths are assigned exactly 1 cause. As part of the GBD study, a map has been developed that allows ICD-9 and ICD-10 codes to be translated to GBD causes Previous studies have documented the existence of insufficiently specific or implausible causes of death used in death registration data that may lead to misleading geographic and temporal patterns. Algorithms developed for the GBD were used to reallocate deaths assigned one of these garbage codes to plausible altematives. First, plausible target causes were assigned to each garbage code or group of garbage codes. Second, deaths were reassigned to specified target codes according to proportions derived in 1 of 4 ways: (1) published literature or expert opinion; (2) regression models; (3) according to the proportions initially observed among targets; and (4) for HIV/AIDS specifically, by comparison to years before HIV/AIDS became widespread. Based on standard GBD methods, YLLs were computed by multiplying the number of deaths from each cause in each age group by the reference life expectancy at the mean of age of death among those who died in the age group The YLLs computation is based on the precedent set by GBD and uses the same life table standard for calculating YLLs in all locations and years (essential for comparing estimates of YLLs across locations and years). The standard is meant to represent the mortality experience of a population with minimal excess mortality using the lowest observed age-specific mortality rates in 2016 among all countries with a population greater than 5 million This standard does not vary with time because for most populations, the number of YLLs (once nomalized for population size) is larger in earlier years than in later years due to improving survival rather than an artifact of the standard used.

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