Assessing the Validity of Statistical Inferences in Public Health Research: An Evidence-Based, ‘Best-Practices’ Approach

Like many fields, public health has embraced the process of evidence-based practice to inform practice decisions and to guide policy development. Evidence-based practice is typically dependent upon generalizations made on the bases of the existing body of knowledge – assimilations of the research literature on a particular topic. The potential utility of scientific evidence for guiding policy and practice decisions is grounded in the validity of the research investigations upon which such decisions are made. However, the validity of inferences made from the extant public health research literature requires more than ascertaining the validity of the statistical methods alone; for each study, the validity of the entire research process must be critically analyzed to the greatest extent possible so that appropriate conclusions can be drawn, and that recommendations for development of sound public health policy and practice can be offered. A critical analysis of the research process should include the following: An a priori commitment to the research question; endpoints that are both appropriate for and consistent with the research question; an experimental design that is appropriate (i.e., that answers the research question[s]); study procedures that are conducted in a quality manner, that eliminate bias and ensure that the data accurately reflect the condition(s) under study; evidence that the integrity of the Type-I error – or false-positive risk – has been preserved; use of appropriate statistical methods (e.g. assumptions checked, dropouts appropriately handled, correct variance term) for the data analyzed; and accurate interpretation of the results of statistical tests conducted in the study (e.g., the robustness of conclusions relative to missing data, multiple endpoints, multiple analyses, conditions of study, generalization of results, etc.). This paper provides a framework for both researcher and practitioner so that each may assess this critical aspect of public health research

Racial Differences in Perception of Breast Cancer Risk in Rural Southeast Georgia

A university-public health collaborative was formed to more fully understand cancer risk among rural women in Georgia. Objectives: This study sought to gain an understanding of racial differences with regard to behavioral risk, perception of breast cancer risk, and perception of barriers to screening. Design: Differences in subjects’ risk and risk perception were assessed by creating, piloting, and administering a written survey at local health departments. Sample: A purposive sample of females enrolled in breast and cervical cancer screening programs in four rural counties in southeast Georgia (n = 147) were surveyed. Subjects were randomly invited to participate. Incentives were provided to enhance participation. Results: White females were significantly more likely than were black females to perceive pollution (OR: 4.63; p = 0.038), smoking (OR: 2.39; p = 0.018), age (OR: 3.01; p = 0.013), and hormone replacement therapy (OR: 3.17; p = 0.005) as factors influencing their breast cancer risk, and to perceive cost as a barrier to screening (OR: 2.89; p = 0.032). From a risk perspective, black females were more likely than white females to have had five-or-more pregnancies (p = 0.005), and to have given birth before age fifteen (p = 0.011). Conclusions: This study provided important baseline data about breast cancer risk necessary in developing effective health promotion programs

Grit Had a Positive Impact on Moderate-to-high Intensity Physical Activity During the COVID-19 Health Crisis

Recently, moderate-to-vigorous intensity physical activity (MVPA) has attracted additional scientific interest (e.g., ACSM recommendations) not only thanks to a) the physical and psychological benefits that can be experienced by the general population following just a single bout, but also b) since high levels of MVPA seem to mitigate the mortality risk associated with high levels of sedentary behavior, such as sitting. Non-pharmaceutical interventions against the COVID-19 (e.g., stay-at-home orders) have altered people’s lifestyles (e.g., physical activity, sitting time). Grit, a higher-order personality trait based on two lower-order components (i.e., perseverance and consistency), is highly predictive of both success and performance. PURPOSE: To examine whether grit influenced MVPA during the first 16 weeks of the COVID health crisis on a general-population sample. METHODS: In total, 191 participants (mage = 37.2, SD = 15.8; 78% female) agreed to participate. Grit (via the 8-item Grit Scale; max. score is 5: extremely gritty; lowest score is 1: not at all gritty) and MVPA (mins/week) data were collected at baseline. Then, MVPA data were collected weekly 16 more times. Monthly MVPAs (i.e., weeks 1-4, 5-8, 9-12, and 13-16) were used as variables in a growth model within a multilevel modeling framework. Grit was used as a predictor of the intercept of MVPA at baseline. Differences based on sex and age were also investigated. RESULTS: On average, male and female respondents differed only trivially on grit and the correlation estimate between grit and age was .03. Using a multilevel growth model, grit and MVPA were related at baseline and MVPA was fairly stable across time. The expected amount of MVPA at baseline was 221.7 minutes/week if grit were equal to zero; however, for each one-unit increase in grit, the expected increase in MVPA was 99.4 minutes/week. Holding grit level constant, the reported MVPA decreased by only about 7 minutes per week across the four months of physical activity data. CONCLUSION: MVPA tended to decrease slightly over time; however, grit had a positive impact on MVPA. That is, people with higher grit scores (regardless of age/sex) tended to have higher MVPA at baseline and, consequently, over time. Findings infer the value of grit during this unique period in this predominately female, highly active, late-thirties sample

Human Influence on the Climate System (Chapter 3)

The AR5 concluded that human influence on the climate system is clear, evident from increasing greenhouse gas concentrations in the atmosphere, positive radiative forcing, observed warming, and physical understanding of the climate system. This chapter updates the assessment of human influence on the climate system for large-scale indicators of climate change, synthesizing information from paleo records, observations and climate models. It also provides the primary evaluation of large-scale indicators of climate change in this Report, complemented by fitness-for-purpose evaluation in subsequent chapters

Circulating Pneumolysin Is a Potent Inducer of Cardiac Injury during Pneumococcal Infection

Streptococcus pneumoniae accounts for more deaths worldwide than any other single pathogen through diverse disease manifestations including pneumonia, sepsis and meningitis. Life-threatening acute cardiac complications are more common in pneumococcal infection compared to other bacterial infections. Distinctively, these arise despite effective antibiotic therapy. Here, we describe a novel mechanism of myocardial injury, which is triggered and sustained by circulating pneumolysin (PLY). Using a mouse model of invasive pneumococcal disease (IPD), we demonstrate that wild type PLY-expressing pneumococci but not PLY-deficient mutants induced elevation of circulating cardiac troponins (cTns), well-recognized biomarkers of cardiac injury. Furthermore, elevated cTn levels linearly correlated with pneumococcal blood counts (r=0.688, p=0.001) and levels were significantly higher in non-surviving than in surviving mice. These cTn levels were significantly reduced by administration of PLY-sequestering liposomes. Intravenous injection of purified PLY, but not a non-pore forming mutant (PdB), induced substantial increase in cardiac troponins to suggest that the pore-forming activity of circulating PLY is essential for myocardial injury in vivo. Purified PLY and PLY-expressing pneumococci also caused myocardial inflammatory changes but apoptosis was not detected. Exposure of cultured cardiomyocytes to PLY-expressing pneumococci caused dose-dependent cardiomyocyte contractile dysfunction and death, which was exacerbated by further PLY release following antibiotic treatment. We found that high PLY doses induced extensive cardiomyocyte lysis, but more interestingly, sub-lytic PLY concentrations triggered profound calcium influx and overload with subsequent membrane depolarization and progressive reduction in intracellular calcium transient amplitude, a key determinant of contractile force. This was coupled to activation of signalling pathways commonly associated with cardiac dysfunction in clinical and experimental sepsis and ultimately resulted in depressed cardiomyocyte contractile performance along with rhythm disturbance. Our study proposes a detailed molecular mechanism of pneumococcal toxin-induced cardiac injury and highlights the major translational potential of targeting circulating PLY to protect against cardiac complications during pneumococcal infections

An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data

Citation: Shi, Z. Z., Chapes, S. K., Ben-Arieh, D., & Wu, C. H. (2016). An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data. Plos One, 11(8), 39. doi:10.1371/journal.pone.0161131We present an agent-based model (ABM) to simulate a hepatic inflammatory response (HIR) in a mouse infected by Salmonella that sometimes progressed to problematic proportions, known as "sepsis". Based on over 200 published studies, this ABM describes interactions among 21 cells or cytokines and incorporates 226 experimental data sets and/or data estimates from those reports to simulate a mouse HIR in silico. Our simulated results reproduced dynamic patterns of HIR reported in the literature. As shown in vivo, our model also demonstrated that sepsis was highly related to the initial Salmonella dose and the presence of components of the adaptive immune system. We determined that high mobility group box-1, C-reactive protein, and the interleukin-10: tumor necrosis factor-a ratio, and CD4+ T cell: CD8+ T cell ratio, all recognized as biomarkers during HIR, significantly correlated with outcomes of HIR. During therapy-directed silico simulations, our results demonstrated that anti-agent intervention impacted the survival rates of septic individuals in a time-dependent manner. By specifying the infected species, source of infection, and site of infection, this ABM enabled us to reproduce the kinetics of several essential indicators during a HIR, observe distinct dynamic patterns that are manifested during HIR, and allowed us to test proposed therapy-directed treatments. Although limitation still exists, this ABM is a step forward because it links underlying biological processes to computational simulation and was validated through a series of comparisons between the simulated results and experimental studies

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis