Lack of validity of self-reported mammography data.

This qualitative literature review aimed to describe the totality of peer-reviewed scientific evidence from 1990 to 2017 concerning validity of self-reported mammography. This review included articles about mammography containing the words accuracy, validity, specificity, sensitivity, reliability or reproducibility; titles containing self-report, recall or patient reports, and breast or 'mammo'; and references of identified citations focusing on evaluation of 2-year self-reports. Of 45 publications meeting the eligibility criteria, 2 conducted in 1993 and 1995 at health maintenance organisations in Western USA which primarily served highly educated whites provided support for self-reports of mammography over 2 years. Methodological concerns about validity of self-reports included (1) telescoping, (2) biased overestimates particularly among black women, (3) failure to distinguish screening and diagnostic mammography, and (4) failure to address episodic versus consistent mammography use. The current totality of evidence supports the need for research to reconsider the validity of self-reported mammography data as well as the feasibility of alternative surveillance data sources to achieve the goals of the Healthy People Initiative.

A multifactorial obesity model developed from nationwide public health exposome data and modern computational analyses.

STATEMENT OF THE PROBLEM: Obesity is both multifactorial and multimodal, making it difficult to identify, unravel and distinguish causative and contributing factors. The lack of a clear model of aetiology hampers the design and evaluation of interventions to prevent and reduce obesity. METHODS: Using modern graph-theoretical algorithms, we are able to coalesce and analyse thousands of inter-dependent variables and interpret their putative relationships to obesity. Our modelling is different from traditional approaches; we make no a priori assumptions about the population, and model instead based on the actual characteristics of a population. Paracliques, noise-resistant collections of highly-correlated variables, are differentially distilled from data taken over counties associated with low versus high obesity rates. Factor analysis is then applied and a model is developed. RESULTS AND CONCLUSIONS: Latent variables concentrated around social deprivation, community infrastructure and climate, and especially heat stress were connected to obesity. Infrastructure, environment and community organisation differed in counties with low versus high obesity rates. Clear connections of community infrastructure with obesity in our results lead us to conclude that community level interventions are critical. This effort suggests that it might be useful to study and plan interventions around community organisation and structure, rather than just the individual, to combat the nation's obesity epidemic.

Exploration of preterm birth rates using the public health exposome database and computational analysis methods.

Recent advances in informatics technology has made it possible to integrate, manipulate, and analyze variables from a wide range of scientific disciplines allowing for the examination of complex social problems such as health disparities. This study used 589 county-level variables to identify and compare geographical variation of high and low preterm birth rates. Data were collected from a number of publically available sources, bringing together natality outcomes with attributes of the natural, built, social, and policy environments. Singleton early premature county birth rate, in counties with population size over 100,000 persons provided the dependent variable. Graph theoretical techniques were used to identify a wide range of predictor variables from various domains, including black proportion, obesity and diabetes, sexually transmitted infection rates, mother's age, income, marriage rates, pollution and temperature among others. Dense subgraphs (paracliques) representing groups of highly correlated variables were resolved into latent factors, which were then used to build a regression model explaining prematurity (R-squared = 76.7%). Two lists of counties with large positive and large negative residuals, indicating unusual prematurity rates given their circumstances, may serve as a starting point for ways to intervene and reduce health disparities for preterm births.

The public health exposome: a population-based, exposure science approach to health disparities research.

The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment, personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures "get under the skin". The public health exposome approach has led our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training.

Scalable combinatorial tools for health disparities research.

Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual's genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size and heterogeneity of available data needed to illuminate latent gene x environment interactions. This has prompted the adaptation and application of scalable combinatorial methods, many from genome science research, to the study of population health. Most of these powerful tools are algorithmically sophisticated, highly automated and mathematically abstract. Their utility motivates the main theme of this paper, which is to describe real applications of innovative transdisciplinary models and analyses in an effort to help move the research community closer toward identifying the causal mechanisms and associated environmental contexts underlying health disparities. The public health exposome is used as a contemporary focus for addressing the complex nature of this subject.

Geographic variations in percutaneous coronary interventions and coronary artery bypass graft surgery among Tennessee elders.

OBJECTIVES: Coronary heart disease often presents with ST segment elevation acute myocardial infarction (STEMI). The American College of Cardiology/American Heart Association guidelines stress prompt reperfusion for STEMI. Examining geographic variations in treatment with PPCI (percutaneous primary coronary intervention) and CABG (coronary artery bypass graft) among metropolitan, micropolitan and non-micropolitan rural residents provides a descriptive basis for generating hypotheses concerning place and receipt of guidelines-based treatment. METHODS: Using ICD-9 codes for STEMI and excluding beneficiaries with pre-existing MI in claims data, yielded a subset of 18,775 Tennessee Medicare beneficiaries experiencing STEMI from 1996 to 2002. The outcome variable is type of treatment, i.e., in accord (PPCI or CABG present) or not in accord (PPCI or CABG absent) with guidelines. Independent variables include type county residence, hospital volume, race, gender, and age. Analyses include cross-tabulation and logistic regression, estimating separate models by age and type of MI. RESULTS: Micropolitan residents with STEMI have the lowest rates for PPCI (18.8%) versus 28.1% percent for metropolitan and 24.2% for non-micropolitan rural residents. CABG follows similar patterns at lower overall rates. Treatment at a heart center with high volume PPCI mediated the relationship between the likelihood of PPCI and place. CONCLUSION: The correspondence between metropolitan and rural utilization suggests that access to a full range of treatment options and likelihood of "best practice" care is not dependent on metropolitan residence. This presents the possibility that with some policy changes, e.g., centralization of emergency heart care, the same may ultimately be true for micropolitan residents.

Descriptive and analytic epidemiologic studies to identify modifiable determinants of disparities in mortality rates between Blacks and Whites.

INTRODUCTION: Descriptive and epidemiologic studies of US national data reveal no sustained decrease in disparities in mortality between Blacks and Whites since World War II. In contrast, descriptive studies of variations in racial mortality trends across comparable geographic areas might lead to the formulation of hypotheses for testing in analytical epidemiologic studies to identify modifiable determinants. METHODOLOGY: Using the CDC Wonder system, the 41 peer-geographic areas to Davidson County, Tennessee, in which Meharry Medical College resides, were identified. For 1999-2000, race- and sex-specific, age-adjusted annual mortality rate ratios (MRRs) for Blacks compared to Whites, stratified by age (<1, 1-24, 25-64, and > or = 65 years), were calculated. In 39 of the 41, MRR was >1.0, indicating higher death rates for Blacks. RESULTS: In 20 counties with low Black:White poverty ratios, MRRs were higher in 18 and achieved statistical significance in 13. In 21 counties with high Black:White poverty ratios, MRRs were higher in 20 counties and achieved statistical significance in 19. No MRRs were significantly lower for Blacks. CONCLUSION: This new pathway includes descriptive data to explore racial mortality trends across peer-geographic areas to formulate hypotheses for analytical epidemiologic studies for the identification of modifiable determinants of disparities in mortality rates between Blacks and Whites.