Educational Degrees and Adult Mortality Risk in the United States

We present the first published estimates of U. S. adult mortality risk by detailed educational degree, including advanced postsecondary degrees. We use the 1997–2002 National Health Interview Survey (NHIS) Linked Mortality Files and Cox proportional hazards models to reveal wide graded differences in mortality by educational degree. Compared to adults who have a professional degree, those with an MA are 5 percent, those with a BA are 26 percent, those with an AA are 44 percent, those with some college are 65 percent, high school graduates are 80 percent, and those with a GED or 12 or fewer years of schooling are at least 95 percent more likely to die during the follow-up period, net of sociodemographic controls. These differentials vary by gender and cohort. Advanced educational degrees are associated not only with increased workforce skill level but with a reduced risk of death.     

Current Estimates of the Economic Cost of Obesity in the United States

This study was undertaken to update and revise the estimate of the economic impact of obesity in the United States. A prevalence-based approach to the cost of illness was used to estimate the economic costs in 1995 dollars attributable toobesity for type 2 diabetes mellitus, coronary heart disease (CHD), hypertension, gallbladder disease, breast, endometrial and colon cancer, and osteoarthritis. Additionally and independently, excess physician visits, work-lost days, restricted activity, and bed-days attributable to obesity were analyzed cross-sectionally using the 1988 and 1994 National Health Interview Survey (NHIS). Direct (personal health care, hospital care, physician services, allied health services, and medications) and indirect costs (lost output as a result of a reduction or cessation of productivity due to morbidity or mortality) are from published reports and inflated to 1995 dollars using the medical component of the consumer price index (CPI) for direct cost and the all-items CPI for indirect cost. Population-attributable risk percents (PAR%) are estimated from large prospective studies. Excess work-lost days, restricted activity, bed-days, and physician visits are estimated from 88,262 U. S. citizens who participated in the 1988 NHIS and 80,261 who participated in the 1994 NHIS. Sample weights have been incorporated into the NHIS analyses, making these data generalizable to the U. S. population. The total cost attributable to obesity amounted to $99. 2 billion dollars in 1995. Approximately $51. 64 billion of those dollars were direct medical costs. Using the 1994 NHIS data, cost of lost productivity attributed to obesity (BMI≥30) was $3. 9 billion and reflected 39. 2 million days of lost work. In addition, 239 million restricted-activity days, 89. 5 million bed-days, and 62. 6 million physician visits were attributable to obesity in 1994. Compared with 1988 NHIS data, in 1994 the number of restricted-activity days (36%), bed-days (28%), and work-lost days (50%) increased substantially. The number of physician visits attributed to obesity increased 88% from 1988 to 1994. The economic and personal health costs of overweight and obesity are enormous and compromise the health of the United States. The direct costs associated with obesity represent 5. 7% of our National Health Expenditure in the United States .     

Racial/Ethnic Differences in Early-Life Mortality in the United States

U.S. early-life (ages 1–24) deaths are tragic, far too common, and largely preventable. Yet demographers have focused scant attention on U.S. early-life mortality patterns, particularly as they vary across racial and ethnic groups. We employed the restricted-use 1999–2011 National Health Interview Survey–Linked Mortality Files and hazard models to examine racial/ethnic differences in early-life mortality. Our results reveal that these disparities are large, strongly related to differences in parental socioeconomic status, and expressed through different causes of death. Compared to non-Hispanic whites, non-Hispanic blacks experience 60 percent and Mexican Americans 32 percent higher risk of death over the follow-up period, with demographic controls. Our finding that Mexican Americans experience higher early-life mortality risk than non-Hispanic whites differs from much of the literature on adult mortality. We also show that these racial/ethnic differences attenuate with controls for family structure and especially with measures of socioeconomic status. For example, higher mortality risk among Mexican Americans than among non-Hispanic whites is no longer significant once we controlled for mother’s education or family income. Our results strongly suggest that eliminating socioeconomic gaps across groups is the key to enhanced survival for children and adolescents in racial/ethnic minority groups.     

Geographic Analysis of Urologist Density and Prostate Cancer Mortality in the United States

Context

Financial and demographic pressures in US require an understanding of the most efficient distribution of physicians to maximize population-level health benefits. Prior work has assumed a constant negative relationship between physician supply and mortality outcomes throughout the US and has not addressed regional variation.

Methods

In this ecological analysis, geographically weighted regression was used to identify spatially varying relationships between local urologist density and prostate cancer mortality at the county level. Data from 1,492 counties in 30 eastern and southern states from 2006–2010 were analyzed.

Findings

The ordinary least squares (OLS) regression found that, on average, increasing urologist density by 1 urologist per 100,000 people resulted in an expected decrease in prostate cancer mortality of -0.499 deaths per 100,000 men (95% CI -0.709 to -0.289, p-value < 0.001), or a 1.5% decrease. Geographic weighted regression demonstrated that the addition of one urologist per 100,000 people in counties in the southern Mississippi River states of Arkansas, Mississippi, and Louisiana, as well as parts of Illinois, Indiana, and Wisconsin is associated with decrease of 0.411 to 0.916 in prostate cancer mortality per 100,000 men (1.6–3.6%). In contrast, the urologist density was not significantly associated with the prostate state mortality in the new England region.

Conclusions

The strength of association between urologist density and prostate cancer mortality varied regionally. Those areas with the highest potential for effects could be targeted for increasing the supply of urologists, as it associated with the largest predicted improvement in prostate cancer mortality.     

Improving Google Flu Trends Estimates for the United States through Transformation

Google Flu Trends (GFT) uses Internet search queries in an effort to provide early warning of increases in influenza-like illness (ILI). In the United States, GFT estimates the percentage of physician visits related to ILI (%ILINet) reported by the Centers for Disease Control and Prevention (CDC). However, during the 2012–13 influenza season, GFT overestimated %ILINet by an appreciable amount and estimated the peak in incidence three weeks late. Using data from 2010–14, we investigated the relationship between GFT estimates (%GFT) and %ILINet. Based on the relationship between the relative change in %GFT and the relative change in %ILINet, we transformed %GFT estimates to better correspond with %ILINet values. In 2010–13, our transformed %GFT estimates were within ±10% of %ILINet values for 17 of the 29 weeks that %ILINet was above the seasonal baseline value determined by the CDC; in contrast, the original %GFT estimates were within ±10% of %ILINet values for only two of these 29 weeks. Relative to the %ILINet peak in 2012–13, the peak in our transformed %GFT estimates was 2% lower and one week later, whereas the peak in the original %GFT estimates was 74% higher and three weeks later. The same transformation improved %GFT estimates using the recalibrated 2013 GFT model in early 2013–14. Our transformed %GFT estimates can be calculated approximately one week before %ILINet values are reported by the CDC and the transformation equation was stable over the time period investigated (2010–13). We anticipate our results will facilitate future use of GFT.     

Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time

Circulating levels of both seasonal and pandemic influenza require constant surveillance to ensure the health and safety of the population. While up-to-date information is critical, traditional surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of estimating, in near-real time, the level of influenza-like illness (ILI) in the United States (US) by monitoring the rate of particular Wikipedia article views on a daily basis. We calculated the number of times certain influenza- or health-related Wikipedia articles were accessed each day between December 2007 and August 2013 and compared these data to official ILI activity levels provided by the Centers for Disease Control and Prevention (CDC). We developed a Poisson model that accurately estimates the level of ILI activity in the American population, up to two weeks ahead of the CDC, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. Wikipedia-derived ILI models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012–2013 influenza season). Wikipedia usage accurately estimated the week of peak ILI activity 17% more often than Google Flu Trends data and was often more accurate in its measure of ILI intensity. With further study, this method could potentially be implemented for continuous monitoring of ILI activity in the US and to provide support for traditional influenza surveillance tools.     

Estimates of Disease Effects on Soybean Yields in the United States 2003 to 2005

Research must focus on management of diseases that cause extensive losses, especially when funds for research are limited. Knowledge of the losses caused by various soybean diseases is essential when prioritizing research budgets. The objective of this project was to compile estimates of soybean yields suppressed due to diseases for each soybean-producing state in the US from 2003 to 2005. The goal was to provide this information to help funding agencies and scientists prioritize research objectives and budgets. Yield suppression due to individual diseases varied among regions in the US, and the total of soybean yields suppressed due to diseases in the US varied among years. Soybean cyst nematode suppressed US soybean yield more during 2003 to 2005 than any other disease. Phytophthora root and stem rot, sudden death syndrome, and seedling diseases ranked in the top four on the list of diseases that suppressed soybean yield during these years. This is the first report of soybean yield suppression due to Asian soybean rust in the United States.     

Encephalitis Hospitalization Rates and Inpatient Mortality in the United States, 2000-2010

Background

Encephalitis rates by etiology and acute-phase outcomes for encephalitis in the 21^st century are largely unknown. We sought to evaluate cause-specific rates of encephalitis hospitalizations and predictors of inpatient mortality in the United States.

Methods

Using the Nationwide Inpatient Sample (NIS) from 2000 to 2010, a retrospective observational study of 238,567 patients (mean [SD] age, 44.8 [24.0] years) hospitalized within non-federal, acute care hospitals in the U.S. with a diagnosis of encephalitis was conducted. Hospitalization rates were calculated using population-level estimates of disease from the NIS and population estimates from the United States Census Bureau. Adjusted odds of mortality were calculated for patients included in the study.

Results

In the U.S. from 2000–2010, there were 7.3±0.2 encephalitis hospitalizations per 100,000 population (95% CI: 7.1–7.6). Encephalitis hospitalization rates were highest among females (7.6±0.2 per 100,000) and those <1 year and >65 years of age with rates of 13.5±0.9 and 14.1±0.4 per 100,000, respectively. Etiology was unknown for approximately 50% of cases. Among patients with identified etiology, viral causes were most common (48.2%), followed by Other Specified causes (32.5%), which included predominantly autoimmune conditions. The most common infectious agents were herpes simplex virus, toxoplasma, and West Nile virus. Comorbid HIV infection was present in 7.7% of hospitalizations. Average length of stay was 11.2 days with mortality of 5.6%. In regression analysis, patients with comorbid HIV/AIDS or cancer had increased odds of mortality (odds ratio [OR]  = 1.70; 95% CI: 1.30–2.22 and OR = 2.26; 95% CI: 1.88–2.71, respectively). Enteroviral, postinfectious, toxic, and Other Specified causes were associated with lower odds vs. herpes simplex encephalitis.

Conclusions

While encephalitis and encephalitis-related mortality impose a considerable burden in the U.S. in the 21^st Century, the reported demographics of hospitalized encephalitis patients may be changing.     

Estimates of the number of mounted pteridophyte specimens in six major herbaria in the United States

A stratified-random sampling method was used to estimate the number of pteridophyte specimens in six of the largest herbaria in the United states (F, GH/A/NEBC, MO, NY, UC, US). The point estimate of the collection size and the 95% confidence intervals for each herbarium were found to be as follows: US 251,000 (±20,000); NY 201,000 (±20,000); MO 160,000 (±15,000); GH/A/NEBC 124,000 (±12,000); UC 119,000 (±9,000); F 97,000 (±10,000). It is hoped that these numbers will help assess the growth of these collections in the future.     

Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments

Understanding the basis for intracellular motion is critical as the field moves toward a deeper understanding of the relation between Brownian forces, molecular crowding, and anisotropic (or isotropic) energetic forcing. Effective forces and other parameters used to summarize molecular motion change over time in live cells due to latent state changes, e.g., changes induced by dynamic micro-environments, photobleaching, and other heterogeneity inherent in biological processes. This study discusses limitations in currently popular analysis methods (e.g., mean square displacement-based analyses) and how new techniques can be used to systematically analyze Single Particle Tracking (SPT) data experiencing abrupt state changes in time or space. The approach is to track GFP tagged chromatids in metaphase in live yeast cells and quantitatively probe the effective forces resulting from dynamic interactions that reflect the sum of a number of physical phenomena. State changes can be induced by various sources including: microtubule dynamics exerting force through the centromere, thermal polymer fluctuations, and DNA-based molecular machines including polymerases and protein exchange complexes such as chaperones and chromatin remodeling complexes. Simulations aiming to show the relevance of the approach to more general SPT data analyses are also studied. Refined force estimates are obtained by adopting and modifying a nonparametric Bayesian modeling technique, the Hierarchical Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT applications. The HDP-SLDS method shows promise in systematically identifying dynamical regime changes induced by unobserved state changes when the number of underlying states is unknown in advance (a common problem in SPT applications). We expand on the relevance of the HDP-SLDS approach, review the relevant background of Hierarchical Dirichlet Processes, show how to map discrete time HDP-SLDS models to classic SPT models, and discuss limitations of the approach. In addition, we demonstrate new computational techniques for tuning hyperparameters and for checking the statistical consistency of model assumptions directly against individual experimental trajectories; the techniques circumvent the need for “ground-truth” and/or subjective information.     

The Influence of Vegetation Height Heterogeneity on Forest and Woodland Bird Species Richness across the United States

Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity. Traditionally, studies have relied primarily on two-dimensional habitat structure to model broad scale species richness. Vegetation vertical structure is increasingly used at local scales. However, the spatial arrangement of vegetation height has never been taken into consideration. Our goal was to examine the efficacies of three-dimensional forest structure, particularly the spatial heterogeneity of vegetation height in improving avian richness models across forested ecoregions in the U.S. We developed novel habitat metrics to characterize the spatial arrangement of vegetation height using the National Biomass and Carbon Dataset for the year 2000 (NBCD). The height-structured metrics were compared with other habitat metrics for statistical association with richness of three forest breeding bird guilds across Breeding Bird Survey (BBS) routes: a broadly grouped woodland guild, and two forest breeding guilds with preferences for forest edge and for interior forest. Parametric and non-parametric models were built to examine the improvement of predictability. Height-structured metrics had the strongest associations with species richness, yielding improved predictive ability for the woodland guild richness models (r² = ∼0.53 for the parametric models, 0.63 the non-parametric models) and the forest edge guild models (r² = ∼0.34 for the parametric models, 0.47 the non-parametric models). All but one of the linear models incorporating height-structured metrics showed significantly higher adjusted-r² values than their counterparts without additional metrics. The interior forest guild richness showed a consistent low association with height-structured metrics. Our results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of forest bird species. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness.     

Ambient Air Temperature,Air Quality and the Timing of Excess Mortality Among Young Men in the United States

Human Ecology - I use data on 187 counties in the United States from 2001 to 2017 to examine local variation in the timing of the excess mortality hump, which is the period in adolescence and young...