IMPORTANCE: Chronic respiratory diseases are an important cause of death and disability in the United States. OBJECTIVE: To estimate age-standardized mortality rates by county from chronic ...respiratory diseases. DESIGN, SETTING, AND PARTICIPANTS: Validated small area estimation models were applied to deidentified death records from the National Center for Health Statistics and population counts from the US Census Bureau, National Center for Health Statistics, and Human Mortality Database to estimate county-level mortality rates from 1980 to 2014 for chronic respiratory diseases. EXPOSURE: County of residence. MAIN OUTCOMES AND MEASURES: Age-standardized mortality rates by county, year, sex, and cause. RESULTS: A total of 4 616 711 deaths due to chronic respiratory diseases were recorded in the United States from January 1, 1980, through December 31, 2014. Nationally, the mortality rate from chronic respiratory diseases increased from 40.8 (95% uncertainty interval UI, 39.8-41.8) deaths per 100 000 population in 1980 to a peak of 55.4 (95% UI, 54.1-56.5) deaths per 100 000 population in 2002 and then declined to 52.9 (95% UI, 51.6-54.4) deaths per 100 000 population in 2014. This overall 29.7% (95% UI, 25.5%-33.8%) increase in chronic respiratory disease mortality from 1980 to 2014 reflected increases in the mortality rate from chronic obstructive pulmonary disease (by 30.8% 95% UI, 25.2%-39.0%, from 34.5 95% UI, 33.0-35.5 to 45.1 95% UI, 43.7-46.9 deaths per 100 000 population), interstitial lung disease and pulmonary sarcoidosis (by 100.5% 95% UI, 5.8%-155.2%, from 2.7 95% UI, 2.3-4.2 to 5.5 95% UI, 3.5-6.1 deaths per 100 000 population), and all other chronic respiratory diseases (by 42.3% 95% UI, 32.4%-63.8%, from 0.51 95% UI, 0.48-0.54 to 0.73 95% UI, 0.69-0.78 deaths per 100 000 population). There were substantial differences in mortality rates and changes in mortality rates over time among counties, and geographic patterns differed by cause. Counties with the highest mortality rates were found primarily in central Appalachia for chronic obstructive pulmonary disease and pneumoconiosis; widely dispersed throughout the Southwest, northern Great Plains, New England, and South Atlantic for interstitial lung disease; along the southern half of the Mississippi River and in Georgia and South Carolina for asthma; and in southern states from Mississippi to South Carolina for other chronic respiratory diseases. CONCLUSIONS AND RELEVANCE: Despite recent declines in mortality from chronic respiratory diseases, mortality rates in 2014 remained significantly higher than in 1980. Between 1980 and 2014, there were important differences in mortality rates and changes in mortality by county, sex, and particular chronic respiratory disease type. These estimates may be helpful for informing efforts to improve prevention, diagnosis, and treatment.
Summary Background Estimation of the number and rate of deaths by age and sex is a key first stage for calculation of the burden of disease in order to constrain estimates of cause-specific mortality ...and to measure premature mortality in populations. We aimed to estimate life tables and annual numbers of deaths for 187 countries from 1970 to 2010. Methods We estimated trends in under-5 mortality rate (children aged 0–4 years) and probability of adult death (15–59 years) for each country with all available data. Death registration data were available for more than 100 countries and we corrected for undercount with improved death distribution methods. We applied refined methods to survey data on sibling survival that correct for survivor, zero-sibling, and recall bias. We separately estimated mortality from natural disasters and wars. We generated final estimates of under-5 mortality and adult mortality from the data with Gaussian process regression. We used these results as input parameters in a relational model life table system. We developed a model to extrapolate mortality to 110 years of age. All death rates and numbers have been estimated with 95% uncertainty intervals (95% UIs). Findings From 1970 to 2010, global male life expectancy at birth increased from 56·4 years (95% UI 55·5–57·2) to 67·5 years (66·9–68·1) and global female life expectancy at birth increased from 61·2 years (60·2–62·0) to 73·3 years (72·8–73·8). Life expectancy at birth rose by 3–4 years every decade from 1970, apart from during the 1990s (increase in male life expectancy of 1·4 years and in female life expectancy of 1·6 years). Substantial reductions in mortality occurred in eastern and southern sub-Saharan Africa since 2004, coinciding with increased coverage of antiretroviral therapy and preventive measures against malaria. Sex-specific changes in life expectancy from 1970 to 2010 ranged from gains of 23–29 years in the Maldives and Bhutan to declines of 1–7 years in Belarus, Lesotho, Ukraine, and Zimbabwe. Globally, 52·8 million (95% UI 51·6–54·1 million) deaths occurred in 2010, which is about 13·5% more than occurred in 1990 (46·5 million 45·7–47·4 million), and 21·9% more than occurred in 1970 (43·3 million 42·2–44·6 million). Proportionally more deaths in 2010 occurred at age 70 years and older (42·8% in 2010 vs 33·1% in 1990), and 22·9% occurred at 80 years or older. Deaths in children younger than 5 years declined by almost 60% since 1970 (16·4 million 16·1–16·7 million in 1970 vs 6·8 million 6·6–7·1 million in 2010), especially at ages 1–59 months (10·8 million 10·4–11·1 million in 1970 vs 4·0 million 3·8–4·2 million in 2010). In all regions, including those most affected by HIV/AIDS, we noted increases in mean ages at death. Interpretation Despite global and regional health crises, global life expectancy has increased continuously and substantially in the past 40 years. Yet substantial heterogeneity exists across age groups, among countries, and over different decades. 179 of 187 countries have had increases in life expectancy after the slowdown in progress in the 1990s. Efforts should be directed to reduce mortality in low-income and middle-income countries. Potential underestimation of achievement of the Millennium Development Goal 4 might result from limitations of demographic data on child mortality for the most recent time period. Improvement of civil registration system worldwide is crucial for better tracking of global mortality. Funding Bill & Melinda Gates Foundation.
Summary Background With 4 years until 2015, it is essential to monitor progress towards Millennium Development Goals (MDGs) 4 and 5. Although estimates of maternal and child mortality were published ...in 2010, an update of estimates is timely in view of additional data sources that have become available and new methods developed. Our aim was to update previous estimates of maternal and child mortality using better data and more robust methods to provide the best available evidence for tracking progress on MDGs 4 and 5. Methods We update the analyses of the progress towards MDGs 4 and 5 from 2010 with additional surveys, censuses, vital registration, and verbal autopsy data. For children, we estimate early neonatal (0–6 days), late neonatal (7–28 days), postneonatal (29–364 days), childhood (ages 1–4 years), and under-5 mortality. We use an improved model for estimating mortality by age under 5 years. For maternal mortality, our updated analysis includes greater than 1000 additional site-years of data. We tested a large set of alternative models for maternal mortality; we used an ensemble model based on the models with the best out-of-sample predictive validity to generate new estimates from 1990 to 2011. Findings Under-5 deaths have continued to decline, reaching 7·2 million in 2011 of which 2·2 million were early neonatal, 0·7 million late neonatal, 2·1 million postneonatal, and 2·2 million during childhood (ages 1–4 years). Comparing rates of decline from 1990 to 2000 with 2000 to 2011 shows that 106 countries have accelerated declines in the child mortality rate in the past decade. Maternal mortality has also continued to decline from 409 100 (uncertainty interval 382 900–437 900) in 1990 to 273 500 (256 300–291 700) deaths in 2011. We estimate that 56 100 maternal deaths in 2011 were HIV-related deaths during pregnancy. Based on recent trends in developing countries, 31 countries will achieve MDG 4, 13 countries MDG 5, and nine countries will achieve both. Interpretation Even though progress on reducing maternal and child mortality in most countries is accelerating, most developing countries will take many years past 2015 to achieve the targets of the MDGs 4 and 5. Similarly, although there continues to be progress on maternal mortality the pace is slow, without any overall evidence of acceleration. Immediate concerted action is needed for a large number of countries to achieve MDG 4 and MDG 5. Funding Bill & Melinda Gates Foundation.
INTRODUCTION: Cancer is a leading cause of morbidity and mortality in the United States and results in a high economic burden. OBJECTIVE: To estimate age-standardized mortality rates by US county ...from 29 cancers. DESIGN AND SETTING: Deidentified death records from the National Center for Health Statistics (NCHS) and population counts from the Census Bureau, the NCHS, and the Human Mortality Database from 1980 to 2014 were used. Validated small area estimation models were used to estimate county-level mortality rates from 29 cancers: lip and oral cavity; nasopharynx; other pharynx; esophageal; stomach; colon and rectum; liver; gallbladder and biliary; pancreatic; larynx; tracheal, bronchus, and lung; malignant skin melanoma; nonmelanoma skin cancer; breast; cervical; uterine; ovarian; prostate; testicular; kidney; bladder; brain and nervous system; thyroid; mesothelioma; Hodgkin lymphoma; non-Hodgkin lymphoma; multiple myeloma; leukemia; and all other cancers combined. EXPOSURE: County of residence. MAIN OUTCOMES AND MEASURES: Age-standardized cancer mortality rates by county, year, sex, and cancer type. RESULTS: A total of 19 511 910 cancer deaths were recorded in the United States between 1980 and 2014, including 5 656 423 due to tracheal, bronchus, and lung cancer; 2 484 476 due to colon and rectum cancer; 1 573 593 due to breast cancer; 1 077 030 due to prostate cancer; 1 157 878 due to pancreatic cancer; 209 314 due to uterine cancer; 421 628 due to kidney cancer; 487 518 due to liver cancer; 13 927 due to testicular cancer; and 829 396 due to non-Hodgkin lymphoma. Cancer mortality decreased by 20.1% (95% uncertainty interval UI, 18.2%-21.4%) between 1980 and 2014, from 240.2 (95% UI, 235.8-244.1) to 192.0 (95% UI, 188.6-197.7) deaths per 100 000 population. There were large differences in the mortality rate among counties throughout the period: in 1980, cancer mortality ranged from 130.6 (95% UI, 114.7-146.0) per 100 000 population in Summit County, Colorado, to 386.9 (95% UI, 330.5-450.7) in North Slope Borough, Alaska, and in 2014 from 70.7 (95% UI, 63.2-79.0) in Summit County, Colorado, to 503.1 (95% UI, 464.9-545.4) in Union County, Florida. For many cancers, there were distinct clusters of counties with especially high mortality. The location of these clusters varied by type of cancer and were spread in different regions of the United States. Clusters of breast cancer were present in the southern belt and along the Mississippi River, while liver cancer was high along the Texas-Mexico border, and clusters of kidney cancer were observed in North and South Dakota and counties in West Virginia, Ohio, Indiana, Louisiana, Oklahoma, Texas, Alaska, and Illinois. CONCLUSIONS AND RELEVANCE: Cancer mortality declined overall in the United States between 1980 and 2014. Over this same period, there were important changes in trends, patterns, and differences in cancer mortality among US counties. These patterns may inform further research into improving prevention and treatment.
IMPORTANCE: In the United States, regional variation in cardiovascular mortality is well-known but county-level estimates for all major cardiovascular conditions have not been produced. OBJECTIVE: To ...estimate age-standardized mortality rates from cardiovascular diseases by county. DESIGN AND SETTING: Deidentified death records from the National Center for Health Statistics and population counts from the US Census Bureau, the National Center for Health Statistics, and the Human Mortality Database from 1980 through 2014 were used. Validated small area estimation models were used to estimate county-level mortality rates from all cardiovascular diseases, including ischemic heart disease, cerebrovascular disease, ischemic stroke, hemorrhagic stroke, hypertensive heart disease, cardiomyopathy, atrial fibrillation and flutter, rheumatic heart disease, aortic aneurysm, peripheral arterial disease, endocarditis, and all other cardiovascular diseases combined. EXPOSURES: The 3110 counties of residence. MAIN OUTCOMES AND MEASURES: Age-standardized cardiovascular disease mortality rates by county, year, sex, and cause. RESULTS: From 1980 to 2014, cardiovascular diseases were the leading cause of death in the United States, although the mortality rate declined from 507.4 deaths per 100 000 persons in 1980 to 252.7 deaths per 100 000 persons in 2014, a relative decline of 50.2% (95% uncertainty interval UI, 49.5%-50.8%). In 2014, cardiovascular diseases accounted for more than 846 000 deaths (95% UI, 827-865 thousand deaths) and 11.7 million years of life lost (95% UI, 11.6-11.9 million years of life lost). The gap in age-standardized cardiovascular disease mortality rates between counties at the 10th and 90th percentile declined 14.6% from 172.1 deaths per 100 000 persons in 1980 to 147.0 deaths per 100 000 persons in 2014 (posterior probability of decline >99.9%). In 2014, the ratio between counties at the 90th and 10th percentile was 2.0 for ischemic heart disease (119.1 vs 235.7 deaths per 100 000 persons) and 1.7 for cerebrovascular disease (40.3 vs 68.1 deaths per 100 000 persons). For other cardiovascular disease causes, the ratio ranged from 1.4 (aortic aneurysm: 3.5 vs 5.1 deaths per 100 000 persons) to 4.2 (hypertensive heart disease: 4.3 vs 17.9 deaths per 100 000 persons). The largest concentration of counties with high cardiovascular disease mortality extended from southeastern Oklahoma along the Mississippi River Valley to eastern Kentucky. Several cardiovascular disease conditions were clustered substantially outside the South, including atrial fibrillation (Northwest), aortic aneurysm (Midwest), and endocarditis (Mountain West and Alaska). The lowest cardiovascular mortality rates were found in the counties surrounding San Francisco, California, central Colorado, northern Nebraska, central Minnesota, northeastern Virginia, and southern Florida. CONCLUSIONS AND RELEVANCE: Substantial differences exist between county ischemic heart disease and stroke mortality rates. Smaller differences exist for diseases of the myocardium, atrial fibrillation, aortic and peripheral arterial disease, rheumatic heart disease, and endocarditis.
Introduction
HIV planning requires granular estimates for the number of people living with HIV (PLHIV), antiretroviral treatment (ART) coverage and unmet need, and new HIV infections by district, or ...equivalent subnational administrative level. We developed a Bayesian small‐area estimation model, called Naomi, to estimate these quantities stratified by subnational administrative units, sex, and five‐year age groups.
Methods
Small‐area regressions for HIV prevalence, ART coverage and HIV incidence were jointly calibrated using subnational household survey data on all three indicators, routine antenatal service delivery data on HIV prevalence and ART coverage among pregnant women, and service delivery data on the number of PLHIV receiving ART. Incidence was modelled by district‐level HIV prevalence and ART coverage. Model outputs of counts and rates for each indicator were aggregated to multiple geographic and demographic stratifications of interest. The model was estimated in an empirical Bayes framework, furnishing probabilistic uncertainty ranges for all output indicators. Example results were presented using data from Malawi during 2016–2018.
Results
Adult HIV prevalence in September 2018 ranged from 3.2% to 17.1% across Malawi's districts and was higher in southern districts and in metropolitan areas. ART coverage was more homogenous, ranging from 75% to 82%. The largest number of PLHIV was among ages 35 to 39 for both women and men, while the most untreated PLHIV were among ages 25 to 29 for women and 30 to 34 for men. Relative uncertainty was larger for the untreated PLHIV than the number on ART or total PLHIV. Among clients receiving ART at facilities in Lilongwe city, an estimated 71% (95% CI, 61% to 79%) resided in Lilongwe city, 20% (14% to 27%) in Lilongwe district outside the metropolis, and 9% (6% to 12%) in neighbouring Dowa district. Thirty‐eight percent (26% to 50%) of Lilongwe rural residents and 39% (27% to 50%) of Dowa residents received treatment at facilities in Lilongwe city.
Conclusions
The Naomi model synthesizes multiple subnational data sources to furnish estimates of key indicators for HIV programme planning, resource allocation, and target setting. Further model development to meet evolving HIV policy priorities and programme need should be accompanied by continued strengthening and understanding of routine health system data.
IMPORTANCE: Substance use disorders, including alcohol use disorders and drug use disorders, and intentional injuries, including self-harm and interpersonal violence, are important causes of early ...death and disability in the United States. OBJECTIVE: To estimate age-standardized mortality rates by county from alcohol use disorders, drug use disorders, self-harm, and interpersonal violence in the United States. DESIGN AND SETTING: Validated small-area estimation models were applied to 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 to estimate county-level mortality rates from 1980 to 2014 for alcohol use disorders, drug use disorders, self-harm, and interpersonal violence. EXPOSURES: County of residence. MAIN OUTCOMES AND MEASURES: Age-standardized mortality rates by US county (N = 3110), year, sex, and cause. RESULTS: Between 1980 and 2014, there were 2 848 768 deaths due to substance use disorders and intentional injuries recorded in the United States. Mortality rates from alcohol use disorders (n = 256 432), drug use disorders (n = 542 501), self-harm (n = 1 289 086), and interpersonal violence (n = 760 749) varied widely among counties. Mortality rates decreased for alcohol use disorders, self-harm, and interpersonal violence at the national level between 1980 and 2014; however, over the same period, the percentage of counties in which mortality rates increased for these causes was 65.4% for alcohol use disorders, 74.6% for self-harm, and 6.6% for interpersonal violence. Mortality rates from drug use disorders increased nationally and in every county between 1980 and 2014, but the relative increase varied from 8.2% to 8369.7%. Relative and absolute geographic inequalities in mortality, as measured by comparing the 90th and 10th percentile among counties, decreased for alcohol use disorders and interpersonal violence but increased substantially for drug use disorders and self-harm between 1980 and 2014. CONCLUSIONS AND RELEVANCE: Mortality due to alcohol use disorders, drug use disorders, self-harm, and interpersonal violence varied widely among US counties, both in terms of levels of mortality and trends. These estimates may be useful to inform efforts to target prevention, diagnosis, and treatment to improve health and reduce inequalities.
Brazil has high burdens of tuberculosis (TB) and HIV, as previously estimated for the 26 states and the Federal District, as well as high levels of inequality in social and health indicators. We ...improved the geographic detail of burden estimation by modelling deaths due to TB and HIV and TB case fatality ratios for the more than 5400 municipalities in Brazil.
This ecological study used vital registration data from the national mortality information system and TB case notifications from the national communicable disease notification system from 2001 to 2015. Mortality due to TB and HIV was modelled separately by cause and sex using a Bayesian spatially explicit mixed effects regression model. TB incidence was modelled using the same approach. Results were calibrated to the Global Burden of Disease Study 2016. Case fatality ratios were calculated for TB.
There was substantial inequality in TB and HIV mortality rates within the nation and within states. National-level TB mortality in people without HIV infection declined by nearly 50% during 2001 to 2015, but HIV mortality declined by just over 20% for males and 10% for females. TB and HIV mortality rates for municipalities in the 90th percentile nationally were more than three times rates in the 10th percentile, with nearly 70% of the worst-performing municipalities for male TB mortality and more than 75% for female mortality in 2001 also in the worst decile in 2015. The same municipality ranking metric for HIV was observed to be between 55% and 61%. Within states, the TB mortality rate ratios by sex for municipalities in the worst decile versus the best decile varied from 1.4 to 2.9, and HIV varied from 1.4 to 4.2. The World Health Organization target case fatality rate for TB of less than 10% was achieved in 9.6% of municipalities for males versus 38.4% for females in 2001 and improved to 38.4% and 56.6% of municipalities for males versus females, respectively, by 2014.
Mortality rates in municipalities within the same state exhibited nearly as much relative variation as within the nation as a whole. Monitoring the mortality burden at this level of geographic detail is critical for guiding precision public health responses.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Previous analyses of diabetes prevalence in the U.S. have considered either only large geographic regions or only individuals in whom diabetes had been diagnosed. We estimated county-level trends in ...the prevalence of diagnosed, undiagnosed, and total diabetes as well as rates of diagnosis and effective treatment from 1999 to 2012.
We used a two-stage modeling procedure. In the first stage, self-reported and biomarker data from the National Health and Nutrition Examination Survey (NHANES) were used to build models for predicting true diabetes status, which were applied to impute true diabetes status for respondents in the Behavioral Risk Factor Surveillance System (BRFSS). In the second stage, small area models were fit to imputed BRFSS data to derive county-level estimates of diagnosed, undiagnosed, and total diabetes prevalence, as well as rates of diabetes diagnosis and effective treatment.
In 2012, total diabetes prevalence ranged from 8.8% to 26.4% among counties, whereas the proportion of the total number of cases that had been diagnosed ranged from 59.1% to 79.8%, and the proportion of successfully treated individuals ranged from 19.4% to 31.0%. Total diabetes prevalence increased in all counties between 1999 and 2012; however, the rate of increase varied widely. Over the same period, rates of diagnosis increased in all counties, while rates of effective treatment stagnated.
Our findings demonstrate substantial disparities in diabetes prevalence, rates of diagnosis, and rates of effective treatment within the U.S. These findings should be used to target high-burden areas and select the right mix of public health strategies.
We estimated the prevalence of any drinking and binge drinking from 2002 to 2012 and heavy drinking from 2005 to 2012 in every US county.
We applied small area models to Behavioral Risk Factor ...Surveillance System data. These models incorporated spatial and temporal smoothing and explicitly accounted for methodological changes to the Behavioral Risk Factor Surveillance System during this period.
We found large differences between counties in all measures of alcohol use: in 2012, any drinking prevalence ranged from 11.0% to 78.7%, heavy drinking prevalence ranged from 2.4% to 22.4%, and binge drinking prevalence ranged from 5.9% to 36.0%. Moreover, there was wide variation in the proportion of all drinkers who engaged in heavy or binge drinking. Heavy and binge drinking prevalence increased in most counties between 2005 and 2012, but the magnitude of change varied considerably.
There are large differences within the United States in levels and recent trends in alcohol use. These estimates should be used as an aid in designing and implementing targeted interventions and to monitor progress toward reducing the burden of excessive alcohol use.
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