A Data-Based Approach to Evaluating Representation by Gender and Affiliation in Key Presentation Formats at the Annual Meeting of the Society for Epidemiologic Research.

The annual meeting of the Society for Epidemiologic Research (SER) is a major forum for sharing new research and promoting the career development of participants. Because of this, evaluating representation in key presentation formats is critical. For the 3,257 presentations identified at the 2015-2017 SER annual meetings, we evaluated presenter characteristics, including gender, affiliation, subject area, and h-index, and representation in 3 highlighted presentation formats: platform talks (n = 382), invited symposium talks (n = 273), and chairing a concurrent contributed session or symposium (n = 188). Data were abstracted from SER records, abstract booklets, and programs. Gender was assessed using GenderChecker software, and h-index was determined using the Scopus application programming interface. Log-binomial models were adjusted for participant characteristics and conference year. In adjusted models, women were less likely than men to present an invited symposium talk (relative risk = 0.60, 95% confidence interval: 0.45, 0.81) compared with all participants with accepted abstracts. Researchers from US public universities, US government institutions, and international institutions were less likely to present a symposium talk or to chair a concurrent contributed session or symposium than were researchers from US private institutions. The research areas that were most represented in platform talks were epidemiologic methods, social epidemiology, and cardiovascular epidemiology. Our findings suggest differences in representation by gender, affiliation, and subject area after accounting for h-index.

Does load management using the acute:chronic workload ratio prevent health problems? A cluster randomised trial of 482 elite youth footballers of both sexes.

BACKGROUND: The acute:chronic workload ratio (ACWR) is commonly used to manage training load in sports, particularly to reduce injury risk. However, despite its extensive application as a prevention intervention, the effectiveness of load management using ACWR has never been evaluated in an experimental study. AIM: To evaluate the effectiveness of a load management intervention designed to reduce the prevalence of health problems among elite youth football players of both sexes. METHODS: We cluster-randomised 34 elite youth football teams (16 females, 18 males) to an intervention group (18 teams) and a control group (16 teams). Intervention group coaches planned all training based on published ACWR load management principles using a commercially available athlete management system for a complete 10-month season. Control group coaches continued to plan training as normal. The prevalence of health problems was measured monthly in both groups using the Oslo Sports Trauma Research Centre Questionnaire on Health Problems. RESULTS: The between-group difference in health problem prevalence (primary outcome) was 1.8%-points (-4.1 to 7.7 %-points; p=0.55) with no reduction in the likelihood of reporting a health problem in the intervention group (relative risk 1.01 (95% CI 0.91 to 1.12); p=0.84) compared with the control group. CONCLUSIONS: We observed no between-group difference, suggesting that this specific load management intervention was not successful in preventing health problems in elite youth footballers. TRIAL REGISTRATION NUMBER: ISRCTN18177140.

A heterogeneity measure for cluster identification with application to disease mapping.

Mapping of disease incidence has long been of importance to epidemiology and public health. In this paper, we consider identification of clusters of spatial units with elevated disease rates and develop a new approach that estimates the relative disease risk in association with potential risk factors and simultaneously identifies clusters corresponding to elevated risks. A heterogeneity measure is proposed to enable the comparison of a candidate cluster and its complement under a pair of complementary models. A quasi-likelihood procedure is developed for estimating the model parameters and identifying the clusters. An advantage of our approach over traditional spatial clustering methods is the identification of clusters that can have arbitrary shapes due to abrupt or noncontiguous changes while accounting for risk factors and spatial correlation. Asymptotic properties of the proposed methodology are established and a simulation study shows empirically sound finite-sample properties. The mapping and clustering of enterovirus 71 infections in Taiwan are carried out for illustration.

Robust distributed lag models using data adaptive shrinkage.

Distributed lag models (DLMs) have been widely used in environmental epidemiology to quantify the lagged effects of air pollution on an outcome of interest such as mortality or cardiovascular events. Generally speaking, DLMs can be applied to time-series data where the current measure of an independent variable and its lagged measures collectively affect the current measure of a dependent variable. The corresponding distributed lag (DL) function represents the relationship between the lags and the coefficients of the lagged exposure variables. Common choices include polynomials and splines. On one hand, such a constrained DLM specifies the coefficients as a function of lags and reduces the number of parameters to be estimated; hence, higher efficiency can be achieved. On the other hand, under violation of the assumption about the DL function, effect estimates can be severely biased. In this article, we propose a general framework for shrinking coefficient estimates from an unconstrained DLM, that are unbiased but potentially inefficient, toward the coefficient estimates from a constrained DLM to achieve a bias-variance trade-off. The amount of shrinkage can be determined in various ways, and we explore several such methods: empirical Bayes-type shrinkage, a hierarchical Bayes approach, and generalized ridge regression. We also consider a two-stage shrinkage approach that enforces the effect estimates to approach zero as lags increase. We contrast the various methods via an extensive simulation study and show that the shrinkage methods have better average performance across different scenarios in terms of mean squared error (MSE).We illustrate the methods by using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) to explore the association between PM$_{10}$, O$_3$, and SO$_2$ on three types of disease event counts in Chicago, IL, from 1987 to 2000.

Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models.

The spread of disease through human populations is complex. The characteristics of disease propagation evolve with time, as a result of a multitude of environmental and anthropic factors, this non-stationarity is a key factor in this huge complexity. In the absence of appropriate external data sources, to correctly describe the disease propagation, we explore a flexible approach, based on stochastic models for the disease dynamics, and on diffusion processes for the parameter dynamics. Using such a diffusion process has the advantage of not requiring a specific mathematical function for the parameter dynamics. Coupled with particle MCMC, this approach allows us to reconstruct the time evolution of some key parameters (average transmission rate for instance). Thus, by capturing the time-varying nature of the different mechanisms involved in disease propagation, the epidemic can be described. Firstly we demonstrate the efficiency of this methodology on a toy model, where the parameters and the observation process are known. Applied then to real datasets, our methodology is able, based solely on simple stochastic models, to reconstruct complex epidemics, such as flu or dengue, over long time periods. Hence we demonstrate that time-varying parameters can improve the accuracy of model performances, and we suggest that our methodology can be used as a first step towards a better understanding of a complex epidemic, in situation where data is limited and/or uncertain.

Age density patterns in patients medical conditions: A clustering approach.

This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature.

Assessing heterogeneous effects and their determinants via estimation of potential outcomes.

When analyzing effect heterogeneity, the researcher commonly opts for stratification or a regression model with interactions. While these methods provide valuable insights, their usefulness can be somewhat limited, since they typically fail to take into account heterogeneity with respect to many dimensions simultaneously, or give rise to models with complex appearances. Based on the potential outcomes framework and through imputation of missing potential outcomes, our study proposes a method for analyzing heterogeneous effects by focusing on treatment effects rather than outcomes. The procedure is easy to implement and generates estimates that take into account heterogeneity with respect to all relevant dimensions at the same time. Results are easily interpreted and can additionally be represented by graphs, showing the overall magnitude and pattern of heterogeneity as well as how this relates to different factors. We illustrate the method both with simulations and by examining heterogeneous effects of obesity on HDL cholesterol in the Malmö Diet and Cancer cardiovascular cohort. Obesity was associated with reduced HDL in almost all individuals, but effects varied with smoking, risky alcohol consumption, higher education, and energy intake, with some indications of non-linear effects. Our approach can be applied by any epidemiologist who wants to assess the role and strength of heterogeneity with respect to a multitude of factors.

Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors.

BACKGROUND: Multiple imputation by chained equations (MICE) requires specifying a suitable conditional imputation model for each incomplete variable and then iteratively imputes the missing values. In the presence of missing not at random (MNAR) outcomes, valid statistical inference often requires joint models for missing observations and their indicators of missingness. In this study, we derived an imputation model for missing binary data with MNAR mechanism from Heckman's model using a one-step maximum likelihood estimator. We applied this approach to improve a previously developed approach for MNAR continuous outcomes using Heckman's model and a two-step estimator. These models allow us to use a MICE process and can thus also handle missing at random (MAR) predictors in the same MICE process. METHODS: We simulated 1000 datasets of 500 cases. We generated the following missing data mechanisms on 30% of the outcomes: MAR mechanism, weak MNAR mechanism, and strong MNAR mechanism. We then resimulated the first three cases and added an additional 30% of MAR data on a predictor, resulting in 50% of complete cases. We evaluated and compared the performance of the developed approach to that of a complete case approach and classical Heckman's model estimates. RESULTS: With MNAR outcomes, only methods using Heckman's model were unbiased, and with a MAR predictor, the developed imputation approach outperformed all the other approaches. CONCLUSIONS: In the presence of MAR predictors, we proposed a simple approach to address MNAR binary or continuous outcomes under a Heckman assumption in a MICE procedure.

Spatial distribution of under five year olds being sick or injured in the past two weeks by district in Pakistan.

Use of GIS to visualize the pattern and distribution of health indices in Pakistan would help in better choreographing of health policy and resource allocation decisions. In this study, district-wise spatial distribution of under five-year old children who got sick or injured in the past two weeks, and health consultation pattern was studied using the Pakistan Social and Living Standards Survey 2014-2015. Sex, urban/rural residency status, and province-based differences in the district-wise distribution of under-five year old children who fell sick/injured in the past two weeks and their having received health consultation for it, were observed. For male children, southwestern districts of Sindh, southeastern districts of Balochistan, central districts of KPK, and one southeastern of Punjab reported the highest percent of children who were sick or injured in the past two weeks. For females the pattern was similar with few exceptions.

The Changing Face of Epidemiology: Gender Disparities in Citations?

BACKGROUND: Female biomedical scientists tend to publish fewer articles as last author than their male colleagues and accrue fewer citations per publication. We seek to understand whether epidemiology follows this pattern. METHODS: We gathered aggregate information on the current gender distribution of epidemiology departments (n = 29 of 71 surveyed), societies (n = 4 of 8), and journal editorial boards (n = 6 of 6) using two online surveys and publicly available online information. Bibliometric data from 4,149 articles published between 2008 and 2012 in six high-impact epidemiology journals were drawn from Web of Science and PubMed. RESULTS: We observed a higher prevalence of female than male doctoral students and epidemiology faculty, particularly at lower faculty ranks. A total of 54% of society members were female. Among editorial boards, all current and emeritus editors-in-chief were male and board membership was largely male (64%). Females were more likely to be first authors, but less likely to be last authors. There were no differences in accrued citations at the 50th percentile by first or last author gender. However, articles with male first and last authors tend to accrue more citations (5.7 citations, 95% CI: 2.1, 9.4), mostly driven by the most highly cited articles. This disparity is not fully explained by potential confounders, including seniority. CONCLUSIONS: We found a greater number of female epidemiologists in early-career positions and further evidence of potential gender disparity in publication metrics in epidemiology. If epidemiology continues to be practiced by a majority of women, it remains to be seen if these patterns will change over time.

Estimating cross-validatory predictive p-values with integrated importance sampling for disease mapping models.

An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is the gold standard for estimating predictive p-values that can flag such divergent regions. However, actual LOOCV is time-consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called integrated importance sampling (iIS), for estimating LOOCV predictive p-values with only Markov chain samples drawn from the posterior based on a full data set. The key step in iIS is that we integrate away the latent variables associated the test observation with respect to their conditional distribution without reference to the actual observation. By following the general theory for importance sampling, the formula used by iIS can be proved to be equivalent to the LOOCV predictive p-value. We compare iIS and other three existing methods in the literature with two disease mapping datasets. Our empirical results show that the predictive p-values estimated with iIS are almost identical to the predictive p-values estimated with actual LOOCV and outperform those given by the existing three methods, namely, the posterior predictive checking, the ordinary importance sampling, and the ghosting method by Marshall and Spiegelhalter (2003). Copyright © 2017 John Wiley & Sons, Ltd.

Does Forest Loss Increase Human Disease? Evidence from Nigeria.

It is estimated that about one quarter of the global disease burden in terms of healthy life years lost and about one quarter of all premature deaths can be attributed to modifiable environmental factors (Pruss-Ustun and Corvalan 2006). Three infectious diseases--diarrhea, respiratory infections, and malaria--account for the largest absolute burden in developing countries with children facing the greatest impacts. There is a growing body of evidence demonstrating the health burden of air and water pollution, as well as important productivity and income effects (see, for example, reviews of the literature in Pattanayak and Pfaff 2009 and Greenstone and Jack 2016). Studies that focus on the impacts of natural resource degradation are fewer. Notably, Garg (2016) provides the first causal estimates of the impact of sustained forest cover on reduced malarial incidence in Indonesia, demonstrating a large and previously understudied cost of forest cover loss. In this paper, we extend this new literature on the health impacts of environmental degradation by estimating the causal impact of forest loss on infectious disease incidence in young children using temporal and spatial variation in the last decade in Nigeria. Our estimation strategy involves geolinking a new high-resolution dataset of global forest change to child-level health data from the Nigeria Demographic and Health Surveys from 2008 and 2013. We find that forest loss significantly increases the incidence of malaria, though it does not affect the incidence of diarrhea and respiratory diseases. The impact of forest loss on malaria is large (one standard deviation of forest loss increases malaria incidence by around 4.5 percent in children under five) and the dynamic pattern of the impact suggests a temporary ecological disturbance consistent with findings in Garg (2016) and the tropical medicine literature.

Health Care Use, Health Behaviors, and Medical Conditions Among Individuals in Same-Sex and Opposite-Sex Partnerships: A Cross-Sectional Observational Analysis of the Medical Expenditures Panel Survey (MEPS), 2003-2011.

BACKGROUND: Prior research documents disparities between sexual minority and nonsexual minority individuals regarding health behaviors and health services utilization. However, little is known regarding differences in the prevalence of medical conditions. OBJECTIVES: To examine associations between sexual minority status and medical conditions. RESEARCH DESIGN: We conducted multiple logistic regression analyses of the Medical Expenditure Panel Survey (2003-2011). We identified individuals who reported being partnered with an individual of the same sex, and constructed a matched cohort of individuals in opposite-sex partnerships. SUBJECTS: A total of 494 individuals in same-sex partnerships and 494 individuals in opposite-sex partnerships. MEASURES: Measures of health risk (eg, smoking status), health services utilization (eg, physician office visits), and presence of 15 medical conditions (eg, cancer, diabetes, arthritis, HIV, alcohol disorders). RESULTS: Same-sex partnered men had nearly 4 times the odds of reporting a mood disorder than did opposite-sex partnered men [adjusted odds ratio (aOR)=3.96; 95% confidence interval (CI), 1.85-8.48]. Compared with opposite-sex partnered women, same-sex partnered women had greater odds of heart disease (aOR=2.59; 95% CI, 1.19-5.62), diabetes (aOR=2.75; 95% CI, 1.10-6.90), obesity (aOR=1.92; 95% CI, 1.26-2.94), high cholesterol (aOR=1.89; 95% CI, 1.03-3.50), and asthma (aOR=1.90; 95% CI, 1.02-1.19). Even after adjusting for sociodemographics, health risk behaviors, and health conditions, individuals in same-sex partnerships had 67% increased odds of past-year emergency department utilization and 51% greater odds of ≥3 physician visits in the last year compared with opposite-sex partnered individuals. CONCLUSIONS: A combination of individual-level, provider-level, and system-level approaches are needed to reduce disparities in medical conditions and health care utilization among sexual minority individuals.

Associations across spatial patterns of disease incidences, socio-demographics, and land use in Finland 1991-2010.

AIMS: The epidemiological aim was to draw a general picture of spatial patterns of diseases, socio-demographics, and land use in Finland to detect possible under-recognized associations between the patterns. The methodological purpose was to compare and combine two statistical techniques to approach the data from different viewpoints. METHODS: Two different statistical methods, the self-organizing map and principal coordinates of neighbor matrices with variation partitioning, were used to search for spatial patterns of 15 non-infectious diseases and 17 direct or indirect risk factors. The dataset was gathered from five Finnish registries and pooled over the years 1991-2010. The statistical unit in the analyses was a municipality (n=303). RESULTS: Variables referring to urban living were related to low incidences of all other diseases but cancer, whereas variables referring to rural living were related to low incidences of cancer and high incidences of other diseases, especially coronary heart disease (CHD), hypertension, diabetes, asthma/chronic obstructive pulmonary disease, and serious mental illnesses at the municipal level. The relationships between diseases other than cancer and risk factors related to socio-demographics and land use variables were stronger than those between cancer and risk factors. CONCLUSIONS: The structuration of spatial patterns was dominated by CHD together with land use features and unemployment rate. The relationship between unemployment and spatial health inequalities was emphasized. On the basis of the present study, it is suggested that large heterogeneous datasets are clustered and analyzed simultaneously with more than one statistical method to recognize the most significant and generalizable results.

[THE MODERN CONDITION AND PROGNOSTIC ESTIMATES OF MEDICAL DEMOGRAPHIC PROCESSES IN THE RUSSIAN FEDERATION].

The prognostic estimates offurther development of medical demographic processes in the Russian Federation are presented on the basis of analysis of long-term patterns of natural science order: The deduction is proposed concerning necessity of focusing attention on issues ofpremature mortality and its preventability.

Epidemiology visualized: the prosecutor's fallacy.

The "prosecutor's fallacy" (the assumption that Pr [probability] (A|B) = Pr (B|A)) arises often in epidemiology but is often unrecognized as such, in part because investigators do not have strong intuitions about what the fallacy means. Here, we help inform such intuitions and remind investigators of this fallacy by using visualizations. In figures, we demonstrate the prosecutor's fallacy, as well as show conditions under which Pr (A|B) can be assumed to be equal to Pr (B|A). Visualizations can help build intuition around statistical concepts such as the prosecutor's fallacy and should be more widely considered as teaching tools.

Potential changes in disease patterns and pharmaceutical use in response to climate change.

As climate change alters environmental conditions, the incidence and global patterns of human diseases are changing. These modifications to disease profiles and the effects upon human pharmaceutical usage are discussed. Climate-related environmental changes are associated with a rise in the incidence of chronic diseases already prevalent in the Northern Hemisphere, for example, cardiovascular disease and mental illness, leading to greater use of associated heavily used Western medications. Sufferers of respiratory diseases may exhibit exacerbated symptoms due to altered environmental conditions (e.g., pollen). Respiratory, water-borne, and food-borne toxicants and infections, including those that are vector borne, may become more common in Western countries, central and eastern Asia, and across North America. As new disease threats emerge, substantially higher pharmaceutical use appears inevitable, especially of pharmaceuticals not commonly employed at present (e.g., antiprotozoals). The use of medications for the treatment of general symptoms (e.g., analgesics) will also rise. These developments need to be viewed in the context of other major environmental changes (e.g., industrial chemical pollution, biodiversity loss, reduced water and food security) as well as marked shifts in human demographics, including aging of the population. To identify, prevent, mitigate, and adapt to potential threats, one needs to be aware of the major factors underlying changes in the use of pharmaceuticals and their subsequent release, deliberately or unintentionally, into the environment. This review explores the likely consequences of climate change upon the use of medical pharmaceuticals in the Northern Hemisphere.