Associations of Medicaid Expansion with Insurance Coverage, Stage at Diagnosis, and Treatment among Patients with Genitourinary Malignant Neoplasms

Importance: Health insurance coverage is associated with improved outcomes in patients with cancer. However, it is unknown whether Medicaid expansion through the Patient Protection and Affordable Care Act (ACA) was associated with improvements in the diagnosis and treatment of patients with genitourinary cancer. Objective: To assess the association of Medicaid expansion with health insurance status, stage at diagnosis, and receipt of treatment among nonelderly patients with newly diagnosed kidney, bladder, or prostate cancer. Design, Setting, and Participants: This case-control study included adults aged 18 to 64 years with a new primary diagnosis of kidney, bladder, or prostate cancer, selected from the National Cancer Database from January 1, 2011, to December 31, 2016. Patients in states that expanded Medicaid were the case group, and patients in nonexpansion states were the control group. Data were analyzed from January 2020 to March 2021. Exposures: State Medicaid expansion status. Main Outcomes and Measures: Insurance status, stage at diagnosis, and receipt of cancer and stage-specific treatments. Cases and controls were compared with difference-in-difference analyses. Results: Among a total of 340552 patients with newly diagnosed genitourinary cancers, 94033 (27.6%) had kidney cancer, 25770 (7.6%) had bladder cancer, and 220749 (64.8%) had prostate cancer. Medicaid expansion was associated with a net decrease in uninsured rate of 1.1 (95% CI, -1.4 to -0.8) percentage points across all incomes and a net decrease in the low-income population of 4.4 (95% CI, -5.7 to -3.0) percentage points compared with nonexpansion states. Expansion was also associated with a significant shift toward early-stage diagnosis in kidney cancer across all income levels (difference-in-difference, 1.4 [95% CI, 0.1 to 2.6] percentage points) and among individuals with low income (difference-in-difference, 4.6 [95% CI, 0.3 to 9.0] percentage points) and in prostate cancer among individuals with low income (difference-in-difference, 3.0 [95% CI, 0.3 to 5.7] percentage points). Additionally, there was a net increase associated with expansion compared with nonexpansion in receipt of active surveillance for low-risk prostate cancer of 4.1 (95% CI, 2.9 to 5.3) percentage points across incomes and 4.5 (95% CI, 0 to 9.0) percentage points among patients in low-income areas. Conclusions and Relevance: These findings suggest that Medicaid expansion was associated with decreases in uninsured status, increases in the proportion of kidney and prostate cancer diagnosed in an early stage, and higher rates of active surveillance in the appropriate, low-risk prostate cancer population. Associations were concentrated in population residing in low-income areas and reinforce the importance of improving access to care to all patients with cancer

Does Morphology Matter? The Fungal-Bacterial Inhibitory Interactions of Candida albicans and Alcaligenes faecalis.

Bacteria and fungi have acquired the ability to interact and survive in many hostile environments both found in nature, as well as, the human body. Candida albicans, an opportunistic fungal pathogen, causes a variety of infections in immunocompromised or immunosuppressed individuals, but also asymptomatically colonizes 80% of the population within the intestinal tract, oral cavity, as well as, the female genitourinary system. A unique capability of C. albicans is the ability to change its morphology from benign circular yeast form, to oval pseudohyphal form, to cylindrical tissue-penetrating hyphal form. Our laboratory has previously identified a bacterium, Alcaligenes faecalis, which displays inhibitory characteristics towards C. albicans. A review of the literature shows that some bacteria have the ability to inhibit C. albicans, but only when in the hyphal form. We therefore wanted to explore if the morphological state of C. albicans dictated the degree of inhibition A. faecalis is able to exude, or simply “does morphology matter?” To determine this effect, a series of both solid and liquid media experiments were performed using a wild-type (able to convert between morphologies) strain of C. albicans, a mutant strain of C. albicans locked into the yeast morphological state, and a mutant strain of C. ablicans locked into the hyphal morphological state. For solid media experiments, the different strains of C. albicans were made into a lawn on agar plates, A. faecalis was spotted onto the lawns, and, after 24 hours, observed for signs of inhibition. For liquid media experiments, C. albicans strains were inoculated alone or co-cultured with A. faecalis for 24 hours and plated to enumerate colony forming units. Our results indicate that: (1) the morphological state of C. albicans is not a determining factor, which is a unique finding compared to other published reports; (2) Both A. faecalis and the closely related A. viscolactis both inhibit C. albicans showing that this is a shared ability among the Alcaligenes genus. (3) that the ability to inhibit C. albicans is thru some form of contact dependent mechanism, as the cell free supernatant of A. faecalis has no inhibitory action. Currently, the exact mechanism for this interaction is unknown, but could be one of the secretion systems bacteria use for interactions with other microbes. As there are very limited treatments for fungal infections and severe side-effects associated with current antifungals, exploiting these mechanisms are medically relevant to human health as they could potentially lead to novel treatments for problematic human fungal pathogens

National and Tennessee Trends in BMI Percentile, Obesity, and Overweight Rates Among Youth Using YRBSS Data 1999-2017

Adolescents in the United States continue to exhibit epidemic proportions of obesity and overweight, contributing to significant morbidity and mortality rates. Obesity and overweight are also found to be associated with other unhealthy behaviors in adolescents, such as physical inactivity and smoking. The Center for Disease Control and Prevention (CDC) reports the annual rates of adolescent obesity in the U.S.; however, comparative trends for the past two decades and comparisons of rates between general U.S. and Tennessee, a tobacco-producing state in the stroke belt, are not available. To compare trends in rates of BMI percentile, overweight, and obesity among adolescents, grades 9th through 12th, between the U.S. and Tennessee during 2003-2017 and identify critical factors associated with them. Both national and Tennessee BMI trends show different patterns from 1999 to 2017; further analysis of covariate factors will provide more information on this difference in trends. We expect to find little variation between the United States and Tennessee when comparing the age of high school obesity rates. However, previous trends in racial and ethnic disparities for BMI percentiles, obesity, and overweight suggest there will be differences among these variables. Preventable chronic diseases should not affect children. The outcomes of childhood obesity are too severe to ignore. Understanding the risk factors, risk behaviors, and prevalence of adolescent obesity is the first step in addressing this public health crisis

Analysis of Birth Rate and Predictors Using Linear Regression Model and Propensity Score Matching Method

Evaluating the effectiveness of an intervention can pose challenges if there is not an adequate control group. The effects of the intervention can be distorted by observable differences in the characteristics of the control and treatment groups. Propensity score matching can be used to confirm the outcomes of an intervention are due to the treatment and not other characteristics that may also explain the intervention effects. Propensity score matching is an advanced statistical technique that uses background information on the characteristics of the study population to establish matched pairs of treated participants and controls. This technique improves the quality of control groups and allowing for a better evaluation of the true effects of an intervention. The purpose of this study was to implement this technique to derive county-level matches across the southeastern United States for existing counties within a single state where future statewide initiatives are planned. Statistical analysis was performed using SAS 9.4 (Cary, NC, USA). A select set of key county-level socio-demographic measures theoretically relevant for deriving appropriate matches was examined. These include the proportion of African Americans in population, population density, and proportion of the female population below poverty level. To derive the propensity-matched counties, a logistic regression model with the state of primary interest as the outcome was conducted. The baseline covariates of interest were included in the model and used to predict the probability of a county being in the state of primary interest; this acts as the propensity score used to derive matched controls. A caliper of 0.2 was used to ensure the ratio of the variance of the linear propensity score in the control group to the variance of the linear propensity score in the treatment group is close to 1. The balance of covariates before and after the propensity score matching were assessed to determine if significant differences in each respective covariate persisted after the propensity score matching. Before matching, a significant difference was found in the proportion of African Americans in control group (21.08%, n=3,450) and treatment group (36.95%, n=230) using the t-test (P\u3c0.0001). The percent of females below poverty level showed significant difference between control and treatment group (P=0.0264). The t-test of population density also showed significant differences between the groups (P=0.0424). After matching, the mean differences for the treated-control groups were all zero for these three covariates and the characteristics were no longer showing any significant differences between the two groups. This study found that the use of propensity score matching methods improved the accuracy of matched controls. Ensuring that the control and treatment counties have statistically similar characteristics is important for improving the rigor of future studies examining county-level outcomes. Propensity score matching does not account for unobserved differences between the treatment and control groups that may affect the observed outcomes; however, it does ensure that the observable characteristics between the groups are statistically similar.This method reduces the threat to internal validity that observable characteristics pose on interventions by matching for these potentially confounding characteristics