Food insecurity and maternal depression in rural, low-income families: A longitudinal investigation

Objective: The purpose of the present study was to examine the relationship between household food insecurity and maternal depression in a rural sample to determine whether food insecurity predicted mothers’ depression over time or vice versa. Design: The study employed a prospective design using three waves of data from ‘Rural Families Speak’, a multi-state study of low-income rural families in the USA. Food insecurity was measured using the Core Food Security Module and depression was measured using the Center for Epidemiologic Studies–Depression Scale. A structural equation model was fit to the data using the AMOS software package. Setting: Sixteen states in the USA (California, Indiana, Kentucky, Louisiana, Massachusetts, Maryland, Michigan, Minnesota, Nebraska, New Hampshire, New York, Ohio, Oregon, South Dakota, West Virginia, Wyoming) between 2000 and 2002. Subjects: Subjects included 413 women with at least one child under the age of 13 years living in the home. Results: Findings based on the 184 subjects with complete data indicated that the causal relationship between household food insecurity and depression is bidirectional (P = 0.034 for causation from depression to food insecurity, P = 0.003 for causation from food insecurity to depression, χ2/df = 1.835, root-mean-square error of approximation = 0.068, comparative fit index = 0.989). Findings based on all 413 subjects after imputation of missing values also indicated bidirectionality. Conclusions: The recursive relationship between food insecurity and depression has implications for US nutrition, mental health and poverty policies. The study highlights the need to integrate programs addressing food insecurity and poor mental health for the population of rural, low-income women

Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction

Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy, and develop new therapies for patients. Currently, the predictive results of deep learning (DL)-based models are better than or as good as standard survival methods, they are often disregarded because of their lack of transparency and little interpretability, which is crucial to their adoption in clinical applications. In this paper, we introduce a novel, easily deployable approach, called EXplainable CEnsored Learning (EXCEL), to iteratively exploit critical variables and simultaneously implement (DL) model training based on these variables. First, on a toy dataset, we illustrate the principle of EXCEL; then, we mathematically analyze our proposed method, and we derive and prove tight generalization error bounds; next, on two semi-synthetic datasets, we show that EXCEL has good anti-noise ability and stability; finally, we apply EXCEL to a variety of real-world survival datasets including clinical data and genetic data, demonstrating that EXCEL can effectively identify critical features and achieve performance on par with or better than the original models. It is worth pointing out that EXCEL is flexibly deployed in existing or emerging models for explainable survival data in the presence of right censoring.Comment: 39 page

Food insecurity and maternal depression in rural, low-income families: A longitudinal investigation

Objective: The purpose of the present study was to examine the relationship between household food insecurity and maternal depression in a rural sample to determine whether food insecurity predicted mothers’ depression over time or vice versa. Design: The study employed a prospective design using three waves of data from ‘Rural Families Speak’, a multi-state study of low-income rural families in the USA. Food insecurity was measured using the Core Food Security Module and depression was measured using the Center for Epidemiologic Studies–Depression Scale. A structural equation model was fit to the data using the AMOS software package. Setting: Sixteen states in the USA (California, Indiana, Kentucky, Louisiana, Massachusetts, Maryland, Michigan, Minnesota, Nebraska, New Hampshire, New York, Ohio, Oregon, South Dakota, West Virginia, Wyoming) between 2000 and 2002. Subjects: Subjects included 413 women with at least one child under the age of 13 years living in the home. Results: Findings based on the 184 subjects with complete data indicated that the causal relationship between household food insecurity and depression is bidirectional (P = 0.034 for causation from depression to food insecurity, P = 0.003 for causation from food insecurity to depression, χ2/df = 1.835, root-mean-square error of approximation = 0.068, comparative fit index = 0.989). Findings based on all 413 subjects after imputation of missing values also indicated bidirectionality. Conclusions: The recursive relationship between food insecurity and depression has implications for US nutrition, mental health and poverty policies. The study highlights the need to integrate programs addressing food insecurity and poor mental health for the population of rural, low-income women

Families or Unrelated: The Evolving Debate in Genetic Association Studies

To help uncover the genetic determinants of complex disease, a scientist often designs an association study using either unrelated subjects or family members within pedigrees. But which of these two subject recruitment paradigms is preferable? This editorial addresses the debate over the relative merits of family- and population-based genetic association studies. We begin by briefly recounting the evolution of genetic epidemiology and the rich crossroads of statistics and genetics. We then detail the arguments for the two aforementioned paradigms in recent and current applications. Finally, we speculate on how the debate may progress with the emergence of next-generation sequencing technologies

Thinking outside the curve, part II: modeling fetal-infant mortality

<p>Abstract</p> <p>Background</p> <p>Greater epidemiologic understanding of the relationships among fetal-infant mortality and its prognostic factors, including birthweight, could have vast public health implications. A key step toward that understanding is a realistic and tractable framework for analyzing birthweight distributions and fetal-infant mortality. The present paper is the second of a two-part series that introduces such a framework.</p> <p>Methods</p> <p>We propose estimating birthweight-specific mortality within each component of a normal mixture model representing a birthweight distribution, the number of components having been determined from the data rather than fixed <it>a priori</it>.</p> <p>Results</p> <p>We address a number of methodological issues related to our proposal, including the construction of confidence intervals for mortality risk at any given birthweight within a component, for odds ratios comparing mortality within two different components from the same population, and for odds ratios comparing mortality within analogous components from two different populations. As an illustration we find that, for a population of white singleton infants, the odds of mortality at 3000 g are an estimated 4.15 times as large in component 2 of a 4-component normal mixture model as in component 4 (95% confidence interval, 2.04 to 8.43). We also outline an extension of our framework through which covariates could be probabilistically related to mixture components. This extension might allow the assertion of approximate correspondences between mixture components and identifiable subpopulations.</p> <p>Conclusions</p> <p>The framework developed in this paper does not require infants from compromised pregnancies to share a common birthweight-specific mortality curve, much less assume the existence of an interval of birthweights over which all infants have the same curve. Hence, the present framework can reveal heterogeneity in mortality that is undetectable via a contaminated normal model or a 2-component normal mixture model.</p

Criminal Offending Among Respondents to Protective Orders: Crime Types and Patterns That Predict Victim Risk

Research has shown that respondents to protective orders have robust criminal histories and that criminal offending behavior often follows issuance of a protective order. Nonetheless, the specific nature of the association between protective orders and criminal offending remains unclear. This study uses two classes of statistical models to more clearly delineate that relationship. The models reveal factors and characteristics that appear to be associated with offending and protective order issuance and provide indications about when a victim is most at risk and when the justice system should be most ready to provide immediate protection

Farmwork-Related Injury Among Farmers 50 Years of Age and Older in Kentucky and South Carolina: A Cohort Study, 2002-2005

Farmers in the U.S. are becoming more diverse; the average age of the farmer is increasing, as is the number of women and minority farm operators. There is limited research on injury risk factors in these special populations of farmers. It is especially important to study the risk factors for injury in these growing and at-risk groups. A longitudinal survey was conducted of farmers (n = 1,394) age 50 and older who resided in Kentucky and South Carolina. The questionnaire was administered by telephone and mail surveys four times between 2002 and 2005 to the fixed cohort of farmers, obtained by convenience sample. Approximately half of the cohort was female, and the majority of the cohort worked less than 40 hours per week. This cohort reported a crude, non-fatal injury rate of 9.3 injured farmers per 100 per year. Farmers reporting chronic bronchitis/emphysema (estimated odds ratio [EOR] = 1.57), back problems (EOR = 1.37), arthritis (EOR = 1.31), 3 to 4 restless nights in the past week (EOR = 2.02), or 5 to 7 restless nights in the past week (EOR = 1.82) were at significantly higher odds of sustaining a farmwork-related injury as calculated by the generalized estimating equations (GEE) regression method Farmers operating equipment on highways (EOR = 1.51) or climbing higher than eight feet (EOR = 1.69) were at significantly higher odds of sustaining a farmwork-related injury, and females were at higher risk of injury when performing animal-related tasks (EOR = 3.00) or crop-related tasks (EOR = 2.21). Identified factors associated with farmwork-related injury should better inform agricultural health policies and guidelines for older farmers, such as policies governing the allowable number of hours worked per week and rest breaks, guidelines that advise appropriate types of farm tasks, and ergonomic engineering advances on farming equipment

CMR of LV non-compaction cardiomyopathy: association of clinical presentation and prognosis with cardiac phenotype

Left ventricular non-compaction (LVNC) is a rare congenital disorder characterized by two layered myocardium; trabeculated (non-compacted) and a non-trabeculated (compacted). LVNC is increasingly being recognized due to better imaging technology as a cause for heart failure and sudden cardiac death; however, data on clinical and imaging characteristics remains limited

PD123319 augments angiotensin II-induced abdominal aortic aneurysms through an AT2 receptor-independent mechanism

BACKGROUND: AT2 receptors have an unclear function on development of abdominal aortic aneurysms (AAAs), although a pharmacological approach using the AT2 receptor antagonist PD123319 has implicated a role. The purpose of the present study was to determine the role of AT2 receptors in AngII-induced AAAs using a combination of genetic and pharmacological approaches. We also defined effects of AT2 receptors in AngII-induced atherosclerosis and thoracic aortic aneurysms. METHODS AND RESULTS: Male AT2 receptor wild type (AT2 +/y) and deficient (AT2 -/y) mice in an LDL receptor -/- background were fed a saturated-fat enriched diet, and infused with either saline or AngII (500 ng/kg/min). AT2 receptor deficiency had no significant effect on systolic blood pressure during AngII-infusion. While AngII infusion induced AAAs, AT2 receptor deficiency did not significantly affect either maximal width of the suprarenal aorta or incidence of AAAs. The AT2 receptor antagonist PD123319 (3 mg/kg/day) and AngII were co-infused into male LDL receptor -/- mice that were either AT2 +/y or -/y. PD123319 had no significant effect on systolic blood pressure in either wild type or AT2 receptor deficient mice. Consistent with our previous findings, PD123319 increased AngII-induced AAAs. However, this effect of PD123319 occurred irrespective of AT2 receptor genotype. Neither AT2 receptor deficiency nor PD123319 had any significant effect on AngII-induced thoracic aortic aneurysms or atherosclerosis. CONCLUSIONS: AT2 receptor deficiency does not affect AngII-induced AAAs, thoracic aortic aneurysms and atherosclerosis. PD123319 augments AngII-induced AAAs through an AT2 receptor-independent mechanism