Getting to the bottom of research funding: Acknowledging the complexity of funding dynamics.

Research funding is an important factor for public science. Funding may affect which research topics get addressed, and what research outputs are produced. However, funding has often been studied simplistically, using top-down or system-led perspectives. Such approaches often restrict analysis to confined national funding landscapes or single funding organizations and instruments in isolation. This overlooks interlinkages, broader funding researchers might access, and trends of growing funding complexity. This paper instead frames a 'bottom-up' approach that analytically distinguishes between increasing levels of aggregation of funding instrument co-use. Funding of research outputs is selected as one way to test this approach, with levels traced via funding acknowledgements (FAs) in papers published 2009-18 by researchers affiliated to Denmark, the Netherlands or Norway, in two test research fields (Food Science, Renewable Energy Research). Three funding aggregation levels are delineated: at the bottom, 'funding configurations' of funding instruments co-used by individual researchers (from single-authored papers with two or more FAs); a middle, 'funding amalgamations' level, of instruments co-used by collaborating researchers (from multi-authored papers with two or more FAs); and a 'co-funding network' of instruments co-used across all researchers active in a research field (all papers with two or more FAs). All three levels are found to include heterogenous funding co-use from inside and outside the test countries. There is also co-funding variety in terms of instrument 'type' (public, private, university or non-profit) and 'origin' (domestic, foreign or supranational). Limitations of the approach are noted, as well as its applicability for future analyses not using paper FAs to address finer details of research funding dynamics.

Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees.

Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature.

The Great Recession worsened blood pressure and blood glucose levels in American adults.

Longitudinal, individual-specific data from the Multi-Ethnic Study of Atherosclerosis (MESA) provide support for the hypothesis that the 2008 to 2010 Great Recession (GR) negatively impacted the health of US adults. Results further advance understanding of the relationship by (i) illuminating hypothesized greater negative impacts in population subgroups exposed to more severe impacts of the GR and (ii) explicitly controlling for confounding by individual differences in age-related changes in health over time. Analyses overcome limitations of prior work by (i) employing individual-level data that avoid concerns about ecological fallacy associated with prior reliance on group-level data, (ii) using four waves of data before the GR to estimate and control for underlying individual-level age-related trends, (iii) focusing on objective, temporally appropriate health outcomes rather than mortality, and (iv) leveraging a diverse cohort to investigate subgroup differences in the GR's impact. Innovative individual fixed-effects modeling controlling for individual-level age-related trajectories yielded substantively important insights: (i) significant elevations post-GR for blood pressure and fasting glucose, especially among those on medication pre-GR, and (ii) reductions in prevalence and intensity of medication use post-GR. Important differences in the effects of the GR are seen across subgroups, with larger effects among younger adults (who are likely still in the labor force) and older homeowners (whose declining home wealth likely reduced financial security, with less scope for recouping losses during their lifetime); least affected were older adults without a college degree (whose greater reliance on Medicare and Social Security likely provided more protection from the recession).

Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies.

A growing number and increasing diversity of factors are available for epidemiological studies. These measures provide new avenues for discovery and prevention, yet they also raise many challenges for adoption in epidemiological investigations. Here, we evaluate 1) designs to investigate diseases that consider heterogeneous and multidimensional indicators of exposure and behavior, 2) the implementation of numerous methods to capture indicators of exposure, and 3) the analytical methods required for discovery and validation. We find that case-control studies have provided insights into genetic susceptibility but are insufficient for characterizing complex effects of environmental factors on disease development. Prospective and two-phase designs are required but must balance extended data collection with follow-up of study participants. We discuss innovations in assessments including the microbiome; mass spectrometry and metabolomics; behavioral assessment; dietary, physical activity, and occupational exposure assessment; air pollution monitoring; and global positioning and individual sensors. We claim the the availability of extensive correlated data raises new challenges in disentangling specific exposures that influence cancer risk from among extensive and often correlated exposures. In conclusion, new high-dimensional exposure assessments offer many new opportunities for environmental assessment in cancer development. Cancer Epidemiol Biomarkers Prev; 26(9); 1370-80. ©2017 AACR.

Africans in the American Labor Market.

The number of migrants to the United States from Africa has grown exponentially since the 1930s. For the first time in America's history, migrants born in Africa are growing at a faster rate than migrants from any other continent. The composition of African-origin migrants has also changed dramatically: in the mid-twentieth century, the majority were white and came from only three countries; but today, about one-fifth are white, and African-origin migrants hail from across the entire continent. Little is known about the implications of these changes for their labor market outcomes in the United States. Using the 2000-2011 waves of the American Community Survey, we present a picture of enormous heterogeneity in labor market participation, sectoral choice, and hourly earnings of male and female migrants by country of birth, race, age at arrival in the United States, and human capital. For example, controlling a rich set of human capital and demographic characteristics, some migrants-such as those from South Africa/Zimbabwe and Cape Verde, who typically enter on employment visas-earn substantial premiums relative to other African-origin migrants. These premiums are especially large among males who arrived after age 18. In contrast, other migrants-such as those from Sudan/Somalia, who arrived more recently, mostly as refugees-earn substantially less than migrants from other African countries. Understanding the mechanisms generating the heterogeneity in these outcomes-including levels of socioeconomic development, language, culture, and quality of education in countries of origin, as well as selectivity of those who migrate-figures prominently among important unresolved research questions.

The effects of mortality on fertility: population dynamics after a natural disaster.

Understanding how mortality and fertility are linked is essential to the study of population dynamics. We investigate the fertility response to an unanticipated mortality shock that resulted from the 2004 Indian Ocean tsunami, which killed large shares of the residents of some Indonesian communities but caused no deaths in neighboring communities. Using population-representative multilevel longitudinal data, we identify a behavioral fertility response to mortality exposure, both at the level of a couple and in the broader community. We observe a sustained fertility increase at the aggregate level following the tsunami, which was driven by two behavioral responses to mortality exposure. First, mothers who lost one or more children in the disaster were significantly more likely to bear additional children after the tsunami. This response explains about 13 % of the aggregate increase in fertility. Second, women without children before the tsunami initiated family-building earlier in communities where tsunami-related mortality rates were higher, indicating that the fertility of these women is an important route to rebuilding the population in the aftermath of a mortality shock. Such community-level effects have received little attention in demographic scholarship.

Nighttime lights time series of tsunami damage, recovery, and economic metrics in Sumatra, Indonesia.

On 26 December 2004, a magnitude 9.2 earthquake off the west coast of the northern Sumatra, Indonesia resulted in 160,000 Indonesians killed. We examine the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light imagery brightness values for 307 communities in the Study of the Tsunami Aftermath and Recovery (STAR), a household survey in Sumatra from 2004 to 2008. We examined night light time series between the annual brightness and extent of damage, economic metrics collected from STAR households and aggregated to the community level. There were significant changes in brightness values from 2004 to 2008 with a significant drop in brightness values in 2005 due to the tsunami and pre-tsunami nighttime light values returning in 2006 for all damage zones. There were significant relationships between the nighttime imagery brightness and per capita expenditures, and spending on energy and on food. Results suggest that Defense Meteorological Satellite Program nighttime light imagery can be used to capture the impacts and recovery from the tsunami and other natural disasters and estimate time series economic metrics at the community level in developing countries.

Effect of village midwife program on contraceptive prevalence and method choice in Indonesia.

Indonesia established its Village Midwife Program in 1989 to combat high rates of maternal mortality. The program's goals were to address gaps in access to reproductive health care for rural women, increase access to and use of family planning services, and broaden the mix of available contraceptive methods. In this study, we use longitudinal data from the Indonesia Family Life Survey to examine the program's effect on contraceptive practice. We find that the program did not affect overall contraceptive prevalence but did affect method choice. Over time, for women using contraceptives, midwives were associated with increased odds of injectable contraceptive use and decreased odds of oral contraceptive and implant use. Although the Indonesian government had hoped that the Village Midwife Program would channel women into using longer-lasting methods, the women's "switching behavior" indicates that the program succeeded in providing additional outlets for and promoting the use of injectable contraceptives.

Links between socio-economic circumstances and changes in smoking behavior in the Mexican population: 2002-2010.

While deleterious consequences of smoking on health have been widely publicized, in many developing countries, smoking prevalence is high and increasing. Little is known about the dynamics underlying changes in smoking behavior. This paper examines socio-economic and demographic characteristics associated with smoking initiation and quitting in Mexico between 2002 and 2010. In addition to the influences of age, gender, education, household economic resources and location of residence, changes in marital status, living arrangements and health status are examined. Drawing data from the Mexican Family Life Survey, a rich population-based longitudinal study of individuals, smoking behavior of individuals in 2002 is compared with their behavior in 2010. Logistic models are used to examine socio-demographic and health factors that are associated with initiating and quitting smoking. There are three main findings. First, part of the relationship between education and smoking reflects the role of economic resources. Second, associations of smoking with education and economic resources differ for females and males. Third, there is considerable heterogeneity in the factors linked to smoking behavior in Mexico indicating that the smoking epidemic may be at different stages in different population subgroups. Mexico has recently implemented fiscal policies and public health campaigns aimed at reducing smoking prevalence and discouraging smoking initiation. These programs are likely to be more effective if they target particular socio-economic and demographic sub-groups.

Fertility Regulation in an Economic Crisis.

Substantial international aid is spent reducing the cost of contraception in developing countries, as part of a larger effort to reduce global fertility and increase investment per child worldwide. The importance for fertility behaviors of keeping contraceptive prices low, however, remains unclear. Targeting of subsidies and insufficient price variation have hindered prior attempts to estimate the effect of monetary and non-monetary contraceptive costs on fertility behavior. Using longitudinal survey data from the Indonesia Family Life Survey, we exploit dramatic variation in prices and incomes that was induced by the economic crisis in the late 1990s to pin down the effect of contraceptive availability and costs as well as household resources on contraceptive use and method choice. The results are unambiguous: monetary costs of contraceptives and levels of family economic resources have a very small (and well-determined) impact on contraceptive use and choice of method.

Assessment of rare BRCA1 and BRCA2 variants of unknown significance using hierarchical modeling.

Current evidence suggests that the genetic risk of breast cancer may be caused primarily by rare variants. However, while classification of protein-truncating mutations as deleterious is relatively straightforward, distinguishing as deleterious or neutral the large number of rare missense variants is a difficult on-going task. In this article, we present one approach to this problem, hierarchical statistical modeling of data observed in a case-control study of contralateral breast cancer (CBC) in which all the participants were genotyped for variants in BRCA1 and BRCA2. Hierarchical modeling permits leverage of information from observed correlations of characteristics of groups of variants with case-control status to infer with greater precision the risks of individual rare variants. A total of 181 distinct rare missense variants were identified among the 705 cases with CBC and the 1,398 controls with unilateral breast cancer. The model identified three bioinformatic hierarchical covariates, align-GV, align-GD, and SIFT scores, each of which was modestly associated with risk. Collectively, the 11 variants that were classified as adverse on the basis of all the three bioinformatic predictors demonstrated a stronger risk signal. This group included five of six missense variants that were classified as deleterious at the outset by conventional criteria. The remaining six variants can be considered as plausibly deleterious, and deserving of further investigation (BRCA1 R866C; BRCA2 G1529R, D2665G, W2626C, E2663V, and R3052W). Hierarchical modeling is a strategy that has promise for interpreting the evidence from future association studies that involve sequencing of known or suspected cancer genes.

Discovery of complex pathways from observational data.

Unraveling complex interactions has been a challenge in epidemiologic research. We introduce a pathway modeling framework that discovers plausible pathways from observational data, and allows estimation of both the net effect of the pathway and the types of interactions occurring among genetic or environmental risk factors. Each discovered pathway structure links combinations of observed variables through intermediate latent nodes to a final node, the outcome. Biologic knowledge can be readily applied in this framework as a prior on pathway structure to give preference to more biologically plausible models, thereby providing more precise estimation of Bayes factors for pathways of greatest interest by Markov Chain Monte Carlo (MCMC) methods.Data were simulated for binary inputs of which only a subset was involved in different pathway topologies. Our algorithm was then used to recover the pathway from the simulated data. The posterior distributions of inputs, pairwise and higher-order interactions, and topologies were obtained by MCMC methods. The evidence in favor of a particular pathway or interaction was summarized using Bayes factors. Our method can correctly identify the risk factors and interactions involved in the simulated pathway. We apply our framework to an asthma case-control data set with polymorphisms in 12 genes.

Psychological Health Before, During, and After an Economic Crisis: Results from Indonesia, 1993 - 2000.

The 1997 Indonesian financial crisis resulted in severe economic dislocation and political upheaval, and the detrimental consequences for economic welfare, physical health, and child education have been established in several studies. The crisis also adversely impacted the psychological well-being of the Indonesian population. Comparing responses of the same individuals interviewed before and after the crisis, we document substantial increases in several different dimensions of psychological distress among male and female adults across the entire age distribution. In addition, the imprint of the crisis can be seen in the differential impacts of the crisis on low education groups, the rural landless, and residents in those provinces that were most affected by the crisis. Elevated levels of psychological distress persist even after indicators of economic well-being such as household consumption had returned to pre-crisis levels, suggesting the deleterious effects of the crisis on the psychological well-being of the Indonesian population may be longer lasting than the impacts on economic well-being.

Assessing uncertainty in spatial exposure models for air pollution health effects assessment.

BACKGROUND: Although numerous epidemiologic studies now use models of intraurban exposure, there has been little systematic evaluation of the performance of different models. OBJECTIVES: In this present article we proposed a modeling framework for assessing exposure model performance and the role of spatial autocorrelation in the estimation of health effects. METHODS: We obtained data from an exposure measurement substudy of subjects from the Southern California Children's Health Study. We examined how the addition of spatial correlations to a previously described unified exposure and health outcome modeling framework affects estimates of exposure-response relationships using the substudy data. The methods proposed build upon the previous work, which developed measurement-error techniques to estimate long-term nitrogen dioxide exposure and its effect on lung function in children. In this present article, we further develop these methods by introducing between- and within-community spatial autocorrelation error terms to evaluate effects of air pollution on forced vital capacity. The analytical methods developed are set in a Bayesian framework where multistage models are fitted jointly, properly incorporating parameter estimation uncertainty at all levels of the modeling process. RESULTS: Results suggest that the inclusion of residual spatial error terms improves the prediction of adverse health effects. These findings also demonstrate how residual spatial error may be used as a diagnostic for comparing exposure model performance.

Effect of exposure to traffic on lung development from 10 to 18 years of age: a cohort study.

BACKGROUND: Whether local exposure to major roadways adversely affects lung-function growth during the period of rapid lung development that takes place between 10 and 18 years of age is unknown. This study investigated the association between residential exposure to traffic and 8-year lung-function growth. METHODS: In this prospective study, 3677 children (mean age 10 years [SD 0.44]) participated from 12 southern California communities that represent a wide range in regional air quality. Children were followed up for 8 years, with yearly lung-function measurements recorded. For each child, we identified several indicators of residential exposure to traffic from large roads. Regression analysis was used to establish whether 8-year growth in lung function was associated with local traffic exposure, and whether local traffic effects were independent of regional air quality. FINDINGS: Children who lived within 500 m of a freeway (motorway) had substantial deficits in 8-year growth of forced expiratory volume in 1 s (FEV(1), -81 mL, p=0.01 [95% CI -143 to -18]) and maximum midexpiratory flow rate (MMEF, -127 mL/s, p=0.03 [-243 to -11), compared with children who lived at least 1500 m from a freeway. Joint models showed that both local exposure to freeways and regional air pollution had detrimental, and independent, effects on lung-function growth. Pronounced deficits in attained lung function at age 18 years were recorded for those living within 500 m of a freeway, with mean percent-predicted 97.0% for FEV1 (p=0.013, relative to >1500 m [95% CI 94.6-99.4]) and 93.4% for MMEF (p=0.006 [95% CI 89.1-97.7]). INTERPRETATION: Local exposure to traffic on a freeway has adverse effects on children's lung development, which are independent of regional air quality, and which could result in important deficits in attained lung function in later life.

Assessment and Prediction of Natural Hazards from Satellite Imagery.

Since 2000, there have been a number of spaceborne satellites that have changed the way we assess and predict natural hazards. These satellites are able to quantify physical geographic phenomena associated with the movements of the earth's surface (earthquakes, mass movements), water (floods, tsunamis, storms), and fire (wildfires). Most of these satellites contain active or passive sensors that can be utilized by the scientific community for the remote sensing of natural hazards over a number of spatial and temporal scales. The most useful satellite imagery for the assessment of earthquake damage comes from high-resolution (0.6 m to 1 m pixel size) passive sensors and moderate resolution active sensors that can quantify the vertical and horizontal movement of the earth's surface. High-resolution passive sensors have been used to successfully assess flood damage while predictive maps of flood vulnerability areas are possible based on physical variables collected from passive and active sensors. Recent moderate resolution sensors are able to provide near real time data on fires and provide quantitative data used in fire behavior models. Limitations currently exist due to atmospheric interference, pixel resolution, and revisit times. However, a number of new microsatellites and constellations of satellites will be launched in the next five years that contain increased resolution (0.5 m to 1 m pixel resolution for active sensors) and revisit times (daily ≤ 2.5 m resolution images from passive sensors) that will significantly improve our ability to assess and predict natural hazards from space.

A survey of current Bayesian gene mapping methods.

Recently, there has been much interest in the use of Bayesian statistical methods for performing genetic analyses. Many of the computational difficulties previously associated with Bayesian analysis, such as multidimensional integration, can now be easily overcome using modern high-speed computers and Markov chain Monte Carlo (MCMC) methods. Much of this new technology has been used to perform gene mapping, especially through the use of multi-locus linkage disequilibrium techniques. This review attempts to summarise some of the currently available methods and the software available to implement these methods.

Fine-scale mapping of disease genes with multiple mutations via spatial clustering techniques.

We present a method to perform fine mapping by placing haplotypes into clusters on the basis of risk. Each cluster has a haplotype "center." Cluster allocation is defined according to haplotype centers, with each haplotype assigned to the cluster with the "closest" center. The closeness of two haplotypes is determined by a similarity metric that measures the length of the shared segment around the location of a putative functional mutation for the particular cluster. Our method allows for missing marker information but still estimates the risks of complete haplotypes without resorting to a one-marker-at-a-time analysis. The dimensionality issues that can occur in haplotype analyses are removed by sampling over the haplotype space, allowing for estimation of haplotype risks without explicitly assigning a parameter to each haplotype to be estimated. In this way, we are able to handle haplotypes of arbitrary size. Furthermore, our clustering approach has the potential to allow us to detect the presence of multiple functional mutations.

Application of Bayesian spatial statistical methods to analysis of haplotypes effects and gene mapping.

We propose a method to analyze haplotype effects using ideas derived from Bayesian spatial statistics. We assume that two haplotypes that are similar to one another in structure are likely to have similar risks, and define a distance metric to specify the appropriate level of closeness between the two haplotypes. Through the choice of distance metric, varying levels of population genetics theory can be incorporated into the modeling process, including some that allow estimation of the location of the disease causing mutation(s). This location can be estimated, along with the other parameters of the model, using Markov chain Monte Carlo (MCMC) estimation methods. We demonstrate the effectiveness of the model on two real datasets, a well-known dataset used to fine-map the gene for cystic fibrosis, and one used to localize the gene for Friedreich's ataxia.