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Introduction Diabetes and chronic kidney diseases are associated with a large health burden in the USA and globally. Objective To estimate age-standardized mortality rates by county from diabetes mellitus and chronic kidney disease. Design and setting Validated small area estimation models were applied to de-identified death records from the National Center for Health Statistics (NCHS) and population counts from the census bureau, NCHS, and the Human Mortality Database to estimate county-level mortality rates from 1980 to 2014 from diabetes mellitus and chronic kidney disease (CKD). Exposures County of residence. Main outcomes and measures Age-standardized mortality rates by county, year, sex, and cause. Results Between 1980 and 2014, 2,067,805 deaths due to diabetes were recorded in the USA. The mortality rate due to diabetes increased by 33.6% (95% UI: 26.5%–41.3%) between 1980 and 2000 and then declined by 26.4% (95% UI: 22.8%–30.0%) between 2000 and 2014. Counties with very high mortality rates were found along the southern half of the Mississippi river and in parts of South and North Dakota, while very low rates were observed in central Colorado, and select counties in the Midwest, California, and southern Florida. A total of 1,659,045 deaths due to CKD were recorded between 1980 and 2014 (477,332 due to diabetes mellitus, 1,056,150 due to hypertension, 122,795 due to glomerulonephritis, and 2,768 due to other causes). CKD mortality varied among counties with very low mortality rates observed in central Colorado as well as some counties in southern Florida, California, and Great Plains states. High mortality rates from CKD were observed in counties throughout much of the Deep South, and a cluster of counties with particularly high rates was observed around the Mississippi river. Conclusions and relevance This study found large inequalities in diabetes and CKD mortality among US counties. The findings provide insights into the root causes of this variation and call for improvements in risk factors, access to medical care, and quality of medical care.

Background Excessive alcohol consumption and alcohol-impaired driving remain significant public health problems, leading to considerable morbidity and mortality, particularly among younger populations. Methods Using data from the Behavioral Risk Factor Surveillance System (BRFSS), we employed a small areas modeling strategy to estimate the county-level annual prevalence of alcohol-impaired driving in every United States county for the years 2002 through 2012, the latest year in which county identifiers were publicly available. Results Alcohol-impaired driving episodes declined from 157.0 million in 2002 (prevalence 3.8%: 95% uncertainty interval [UI], 3.7%–4.0%) to 129.7 million in 2012 (prevalence 3.7%: 95% UI, 3.5%–3.8%), a 17.4% decline. There is considerable variation in the prevalence of alcohol-impaired driving at the county level, ranging from 2.0% in the Sitka City Borough of Alaska to 9.3% in Nance County, Nebraska. Clusters of increased alcohol-impaired driving were observed in Northern Wisconsin (Marinette, Florence, Forest, Vilas, Oneida, Iron counties), North Dakota (Cavalier, Pembina, Walsh, Ramsey, Nelson, Benson, Eddy counties) and Montana (Sheridan, Daniels, Roosevelt, Valley, Phillips, Petroleum, Garfield counties). Conclusions This study showed guarded progress with respect to the occurrence of alcohol-impaired driving episodes in the US from 2002 to 2012. Because these data rely on self-report, this likely represents an underestimate of the true prevalence of alcohol-impaired driving in the US. As the US continues to have several million episodes of alcohol-impaired driving each month, renewed efforts are needed to mitigate this high-risk health behavior.

Background Metrics based on self-reports of health status have been proposed for tracking population health and making comparisons among different populations. While these metrics have been used in the US to explore disparities by sex, race/ethnicity, and socioeconomic position, less is known about how self-reported health varies geographically. This study aimed to describe county-level trends in the prevalence of poor self-reported health and to assess the face validity of these estimates. Methods We applied validated small area estimation methods to Behavioral Risk Factor Surveillance System data to estimate annual county-level prevalence of four measures of poor self-reported health (low general health, frequent physical distress, frequent mental distress, and frequent activity limitation) from 1995 and 2012. We compared these measures of poor self-reported health to other population health indicators, including risk factor prevalence (smoking, physical inactivity, and obesity), chronic condition prevalence (hypertension and diabetes), and life expectancy. Results We found substantial geographic disparities in poor self-reported health. Counties in parts of South Dakota, eastern Kentucky and western West Virginia, along the Texas-Mexico border, along the southern half of the Mississippi river, and in southern Alabama generally experienced the highest levels of poor self-reported health. At the county level, there was a strong positive correlation among the four measures of poor self-reported health and between the prevalence of poor self-reported health and the prevalence of risk factors and chronic conditions. There was a strong negative correlation between prevalence of poor self-reported health and life expectancy. Nonetheless, counties with similar levels of poor self-reported health experienced life expectancies that varied by several years. Changes over time in life expectancy were only weakly correlated with changes in the prevalence of poor self-reported health. Conclusions This analysis adds to the growing body of literature documenting large geographic disparities in health outcomes in the United States. Health metrics based on self-reports of health status can and should be used to complement other measures of population health, such as life expectancy, to identify high need areas, efficiently allocate resources, and monitor geographic disparities. Electronic supplementary material The online version of this article (doi:10.1186/s12963-017-0133-5) contains supplementary material, which is available to authorized users.

Background Health has improved markedly in Mesoamerica, the region consisting of southern Mexico and Central America, over the past decade. Despite this progress, there remain substantial inequalities in health outcomes, access, and quality of medical care between and within countries. Poor, indigenous, and rural populations have considerably worse health indicators than national or regional averages. In an effort to address these health inequalities, the Salud Mesoamérica 2015 Initiative (SM2015), a results-based financing initiative, was established. Methods For each of the eight participating countries, health targets were set to measure the progress of improvements in maternal and child health produced by the Initiative. To establish a baseline, we conducted censuses of 90,000 households, completed 20,225 household interviews, and surveyed 479 health facilities in the poorest areas of Mesoamerica. Pairing health facility and household surveys allows us to link barriers to care and health outcomes with health system infrastructure components and quality of health services. Results Indicators varied significantly within and between countries. Anemia was most prevalent in Panama and least prevalent in Honduras. Anemia varied by age, with the highest levels observed among children aged 0 to 11 months in all settings. Belize had the highest proportion of institutional deliveries (99%), while Guatemala had the lowest (24%). The proportion of women with four antenatal care visits with a skilled attendant was highest in El Salvador (90%) and the lowest in Guatemala (20%). Availability of contraceptives also varied. The availability of condoms ranged from 83% in Nicaragua to 97% in Honduras. Oral contraceptive pills and injectable contraceptives were available in just 75% of facilities in Panama. IUDs were observed in only 21.5% of facilities surveyed in El Salvador. Conclusions These data provide a baseline of much-needed information for evidence-based action on health throughout Mesoamerica. Our baseline estimates reflect large disparities in health indicators within and between countries and will facilitate the evaluation of interventions and investments deployed in the region over the next three to five years. SM2015’s innovative monitoring and evaluation framework will allow health officials with limited resources to identify and target areas of greatest need. Electronic supplementary material The online version of this article (doi:10.1186/s12963-015-0034-4) contains supplementary material, which is available to authorized users.

“Prevention is better than treatment”, says an old Arab proverb, yet unfortunately for many Arabs today, medical treatment is the main method of dealing with diseases and risk factors. In a recent Population Health Metrics publication, “The diabetes-obesity-hypertension nexus in Qatar: evidence from the World Health Survey”, [1] Ali et al. analyzed data from the World Health Organization’s World Health Survey conducted in Qatar in 2006. The findings revealed remarkably high prevalences of obesity (35.4%), diabetes (16.4%), and hypertension (18.7%). These levels of established risk factors for major vascular diseases are all the more alarming in a young population. Indeed, the findings call for urgent and greatly strengthened non-communicable disease prevention as a priority in the country and, more broadly, in the region. The study, admittedly, has some limitations. It is crosssectional and therefore not able to establish causation. Furthermore, it is possible that the authors may have overestimated the burden of chronic diseases and levels of risk factor exposure since survey nonrespondents may be healthier than respondents; in the Gulf region, individuals with medical conditions may be more likely to respond to health surveys for a number of reasons. Moreover, the study is based on data from 2006, although the timeliness of the data may not be as great a concern as initially thought. Qatar recently released the findings of its WHO STEPwise approach to surveillance of non-communicable diseases (STEPS) 2012 survey, which showed comparably high if not even higher levels of risk factor exposure for non-communicable diseases [2], with reported prevalences of 41.4% for obesity, 16.7% for diabetes, and 32.9% for hypertension. While the results from the two studies are

Background Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions. Methods We used data on the distribution of exposure to 12 major behavioral and biometric risk factors inthe US population, mortality rates by cause, and estimates of the proportional hazards of risk factor exposure from published systematic reviews to develop a risk prediction model that estimates an adult’s 10 year mortality risk compared to a population with optimum risk factors. We compared predicted risk to observed mortality in 8,241 respondents in NHANES 1988-1994 and NHANES 1999-2004 with linked mortality data up to the end of 2006. Results Predicted risk showed good discrimination with an area under the receiver operating characteristic (ROC) curve of 0.84 (standard error 0.01) for women and 0.84 (SE 0.01) for men. Across deciles of predicted risk, mortality was accurately predicted in men ((Χ2 statistic = 12.3 for men, p=0.196) but slightly overpredicted in the highest decile among women (Χ2 statistic = 22.8, p=0.002). Mortality risk was highly concentrated; for example, among those age 30-44 years, 5.1 % (95 % CI 4.1 % - 6.0 %) of the male and 5.9 % (95 % CI 4.8 % - 6.9 %) of the female population accounted for 25 % of the risk of death. Conclusion The risk model accurately predicted mortality in a representative sample of the US population and could be used to help inform patient and provider decision-making, identify high risk groups, and monitor the impact of efforts to improve population health. Electronic supplementary material The online version of this article (doi:10.1186/s12963-015-0059-8) contains supplementary material, which is available to authorized users.

Background Cigarette smoking is a leading risk factor for morbidity and premature mortality in the United States, yet information about smoking prevalence and trends is not routinely available below the state level, impeding local-level action. Methods We used data on 4.7 million adults age 18 and older from the Behavioral Risk Factor Surveillance System (BRFSS) from 1996 to 2012. We derived cigarette smoking status from self-reported data in the BRFSS and applied validated small area estimation methods to generate estimates of current total cigarette smoking prevalence and current daily cigarette smoking prevalence for 3,127 counties and county equivalents annually from 1996 to 2012. We applied a novel method to correct for bias resulting from the exclusion of the wireless-only population in the BRFSS prior to 2011. Results Total cigarette smoking prevalence varies dramatically between counties, even within states, ranging from 9.9% to 41.5% for males and from 5.8% to 40.8% for females in 2012. Counties in the South, particularly in Kentucky, Tennessee, and West Virginia, as well as those with large Native American populations, have the highest rates of total cigarette smoking, while counties in Utah and other Western states have the lowest. Overall, total cigarette smoking prevalence declined between 1996 and 2012 with a median decline across counties of 0.9% per year for males and 0.6% per year for females, and rates of decline for males and females in some counties exceeded 3% per year. Statistically significant declines were concentrated in a relatively small number of counties, however, and more counties saw statistically significant declines in male cigarette smoking prevalence (39.8% of counties) than in female cigarette smoking prevalence (16.2%). Rates of decline varied by income level: counties in the top quintile in terms of income experienced noticeably faster declines than those in the bottom quintile. Conclusions County-level estimates of cigarette smoking prevalence provide a unique opportunity to assess where prevalence remains high and where progress has been slow. These estimates provide the data needed to better develop and implement strategies at a local and at a state level to further reduce the burden imposed by cigarette smoking.

Background The United States spends more than any other country on health care. The poor relative performance of the US compared to other high-income countries has attracted attention and raised questions about the performance of the US health system. An important dimension to poor national performance is the large disparities in life expectancy. Methods We applied a mixed effects Poisson statistical model and Gaussian Process Regression to estimate age-specific mortality rates for US counties from 1985 to 2010. We generated uncertainty distributions for life expectancy at each age using standard simulation methods. Results Female life expectancy in the United States increased from 78.0 years in 1985 to 80.9 years in 2010, while male life expectancy increased from 71.0 years in 1985 to 76.3 years in 2010. The gap between female and male life expectancy in the United States was 7.0 years in 1985, narrowing to 4.6 years in 2010. For males at the county level, the highest life expectancy steadily increased from 75.5 in 1985 to 81.7 in 2010, while the lowest life expectancy remained under 65. For females at the county level, the highest life expectancy increased from 81.1 to 85.0, and the lowest life expectancy remained around 73. For male life expectancy at the county level, there have been three phases in the evolution of inequality: a period of rising inequality from 1985 to 1993, a period of stable inequality from 1993 to 2002, and rising inequality from 2002 to 2010. For females, in contrast, inequality has steadily increased during the 25-year period. Compared to only 154 counties where male life expectancy remained stagnant or declined, 1,405 out of 3,143 counties (45%) have seen no significant change or a significant decline in female life expectancy from 1985 to 2010. In all time periods, the lowest county-level life expectancies are seen in the South, the Mississippi basin, West Virginia, Kentucky, and selected counties with large Native American populations. Conclusions The reduction in the number of counties where female life expectancy at birth is declining in the most recent period is welcome news. However, the widening disparities between counties and the slow rate of increase compared to other countries should be viewed as a call for action. An increased focus on factors affecting health outcomes, morbidity, and mortality such as socioeconomic factors, difficulty of access to and poor quality of health care, and behavioral, environmental, and metabolic risk factors is urgently required.

Reducing and preventing road traffic injuries (RTIs) is a challenge faced by all nations [1]. The manuscript by Naghavi et al. highlights the huge burden of injuries in Iranian children [2], most of which are the result of road traffic injuries. The manuscript revealed a rising death rate from injuries among children from 1971 to 2005. The authors' previous work has documented the fact that Iran has the highest RTI death rate of any country where such data are available [3,4]. The authors described and documented the main reasons for the rapid increase in RTIs. It is not a surprise that Iran's subsidized gas, which is 10 times cheaper than its production cost, and the rapid increase in vehicle production (more than 1 million cars and 1.5 million motorcycles produced every year since 2002) are important contributing factors in Iran's RTI burden [4]. Indeed, when it comes to RTIs, Iran is experiencing one of the downsides of joining the club of countries with rapid economic growth over the past three decades. The manuscript has some limitations. The comparison to previous years is weakened by the limited amount of historical data. Only data on the capital Tehran were available in 1971. In addition, improvements in the quality of data throughout the study period make the data difficult to compare to previous years. The most important limitation is the completeness of the data and whether the increases in RTI rates are due to better statistics about RTIs rather than true rates of increase. As much as the latter is true, it still does not explain all the increases in RTIs in Iran. However, the results of the study and the previous work of the authors provide a clear picture of RTIs in Iran today and their increasing burden. In general, the study shows the importance of the data systems that capture the burden of injuries in Iran and suggests such systems could be a model for other countries with similar economic growth. Strategies to prevent traffic injuries have been discussed and promoted in many publications [1,5]. These measures include designing safer cars and roads and enforcement of speed and weight limits. In addition, enforcing occupant protection through child safety seats, seat belts, and helmet use, to name a few, is essential. Unfortunately, Iran has a long way to go to reach these objectives. Iran is a country with deep cultural roots; however, it is a late bloomer when it comes to modern mechanization. Iran's rapid increase in motor vehicle use may have started in the 1950s, with its first automobile production occurring in the late 1960s. Car production started its peak in 2002 [4]. This would place Iran about 80 years behind the US and much of Europe in motor vehicle use. What Iran is facing now is similar in many ways to what the US once faced. At the beginning of the century, motor travel in the US was a novelty, with an estimated 8,000 automobiles on the road. By 2000, more than 226 million vehicles were registered [6]. As the number of vehicles and drivers in the US increased, so did deaths and injuries - from 1.0 motor-vehicle deaths per 100,000 to 26.7 per 100,000 in 1930 [6]. For the most part, these rates remained very high until 1970 (27.7 per 100,000), except for a brief decline during World War II due to lack of fuel availability [6]. After 1970, rates began to decline. It took the US about 70 years to curb the burden of RTIs. Iran and other countries should not follow this model. The victories of motor vehicle safety in the US are well-documented [7]. Substantial gains in driver education, vehicle safety, and road design were implemented through legislation and public awareness campaigns. Above all, these victories were due to the work of public safety champions. President Herbert Hoover convened the first National Conference on Street and Highway Safety in 1924. President Franklin D. Roosevelt convened an Accident Prevention Conference on vehicle safety in 1936 and called for lower speeds, better lighting, and stronger auto frames. President Lyndon B. Johnson signed the National Traffic and Motor Safety Act and the Highway Safety Act in 1966. Lawyer and activist Ralph Nader led a consumer movement for vehicle safety in the 1960s. His work inspired hundreds of activists who joined together to help with his work (they came to be known as "Nader's raiders"). William Haddon, a public health physician and epidemiologist who became the first director of the National Highway Safety Bureau, revolutionized the scientific approach to preventing motor-vehicle injuries by developing the Haddon Matrix [8]. His work recognized that injuries are like infectious diseases, a result of the interaction between a host (person), an agent (motor vehicle), and the environment (road). The US success story was built upon the hard work of champions who pushed to reduce RTIs and to improve motor-vehicle safety. As in many success stories, many components must come together to achieve the goal of safer roads. However, the essential ingredient is the dedication and vision of the champions who make motor-vehicle safety their cause. Iran is in dire need of champions such as those who have driven road safety improvements in the US. Their contributions will no doubt expedite the time it takes to achieve a success story similar to what the US has experienced. It is time for champions of RTI prevention and control and motor-vehicle safety to step forward in Iran. The manuscript by Naghavi et al. makes a strong case for Iran to follow the US's path, but at much faster pace.

Background Obesity and physical inactivity are associated with several chronic conditions, increased medical care costs, and premature death. Methods We used the Behavioral Risk Factor Surveillance System (BRFSS), a state-based random-digit telephone survey that covers the majority of United States counties, and the National Health and Nutrition Examination Survey (NHANES), a nationally representative sample of the US civilian noninstitutionalized population. About 3.7 million adults aged 20 years or older participated in the BRFSS from 2000 to 2011, and 30,000 adults aged 20 or older participated in NHANES from 1999 to 2010. We calculated body mass index (BMI) from self-reported weight and height in the BRFSS and adjusted for self-reporting bias using NHANES. We calculated self-reported physical activity—both any physical activity and physical activity meeting recommended levels—from self-reported data in the BRFSS. We used validated small area estimation methods to generate estimates of obesity and physical activity prevalence for each county annually for 2001 to 2011. Results Our results showed an increase in the prevalence of sufficient physical activity from 2001 to 2009. Levels were generally higher in men than in women, but increases were greater in women than men. Counties in Kentucky, Florida, Georgia, and California reported the largest gains. This increase in level of activity was matched by an increase in obesity in almost all counties during the same time period. There was a low correlation between level of physical activity and obesity in US counties. From 2001 to 2009, controlling for changes in poverty, unemployment, number of doctors per 100,000 population, percent rural, and baseline levels of obesity, for every 1 percentage point increase in physical activity prevalence, obesity prevalence was 0.11 percentage points lower. Conclusions Our study showed that increased physical activity alone has a small impact on obesity prevalence at the county level in the US. Indeed, the rise in physical activity levels will have a positive independent impact on the health of Americans as it will reduce the burden of cardiovascular diseases and diabetes. Other changes such as reduction in caloric intake are likely needed to curb the obesity epidemic and its burden.

Background Local measurements of health behaviors, diseases, and use of health services are critical inputs into local, state, and national decision-making. Small area measurement methods can deliver more precise and accurate local-level information than direct estimates from surveys or administrative records, where sample sizes are often too small to yield acceptable standard errors. However, small area measurement requires careful validation using approaches other than conventional statistical methods such as in-sample or cross-validation methods because they do not solve the problem of validating estimates in data-sparse domains. Methods A new general framework for small area estimation and validation is developed and applied to estimate Type 2 diabetes prevalence in US counties using data from the Behavioral Risk Factor Surveillance System (BRFSS). The framework combines the three conventional approaches to small area measurement: (1) pooling data across time by combining multiple survey years; (2) exploiting spatial correlation by including a spatial component; and (3) utilizing structured relationships between the outcome variable and domain-specific covariates to define four increasingly complex model types - coined the Naive, Geospatial, Covariate, and Full models. The validation framework uses direct estimates of prevalence in large domains as the gold standard and compares model estimates against it using (i) all available observations for the large domains and (ii) systematically reduced sample sizes obtained through random sampling with replacement. At each sampling level, the model is rerun repeatedly, and the validity of the model estimates from the four model types is then determined by calculating the (average) concordance correlation coefficient (CCC) and (average) root mean squared error (RMSE) against the gold standard. The CCC is closely related to the intraclass correlation coefficient and can be used when the units are organized in groups and when it is of interest to measure the agreement between units in the same group (e.g., counties). The RMSE is often used to measure the differences between values predicted by a model or an estimator and the actually observed values. It is a useful measure to capture the precision of the model or estimator. Results All model types have substantially higher CCC and lower RMSE than the direct, single-year BRFSS estimates. In addition, the inclusion of relevant domain-specific covariates generally improves predictive validity, especially at small sample sizes, and their leverage can be equivalent to a five- to tenfold increase in sample size. Conclusions Small area estimation of important health outcomes and risk factors can be improved using a systematic modeling and validation framework, which consistently outperformed single-year direct survey estimates and demonstrated the potential leverage of including relevant domain-specific covariates compared to pure measurement models. The proposed validation strategy can be applied to other disease outcomes and risk factors in the US as well as to resource-scarce situations, including low-income countries. These estimates are needed by public health officials to identify at-risk groups, to design targeted prevention and intervention programs, and to monitor and evaluate results over time.