Disparities in Breast Cancer Stage at Diagnosis: Importance of Race, Poverty, and Age

This study investigated the association of race, age, and census tract area poverty level on breast cancer stage at diagnosis. The study was limited to women residing in Missouri, aged 18 years and older, diagnosed with breast cancer, and whose cases were reported to the Cancer Registry between 2003 and 2008. The risk, relative risk, and increased risk of late-stage at diagnosis by race, age, and census tract area poverty level were computed. We found that the odds of late-stage breast cancer among African-American women were higher when compared with their white counterpart (OR 1.433; 95% CI, 1.316, 1.560). In addition, the odds of advanced stage disease for women residing in high-poverty areas were greater than those living in low-poverty areas (OR 1.319; 95% CI 1.08; 1.201). To close the widening cancer disparities gap in Missouri, there is the need for effective and programmatic strategies to enable interventions to reach areas and populations most vulnerable to advanced stage breast cancer diagnosis

Predicting Smoking Behaviors Among Junior High School Students in Ghana

Despite the rising rate of smoking in sub-Sahara African countries, measures to control the tobacco epidemic have been limited to developed countries. The purpose of the present study was to recommend predictive models for determining predictors of smoking tendencies among junior high school students in Ghana. The 2009 Global Youth Tobacco Survey (GYTS) served as the data source. The GYTS is a school-based survey designed to enhance the ability of countries to monitor tobacco use among youth and to guide the implementation and evaluation of tobacco control and prevention programs. Logit model and forward selection were used to choose predictive variables for smoking tendencies and behaviors. Receiver Operating Characteristic (ROC) curve, Area under the curve (AUC) and C-Index were validation tools used to assess the predictive power of recommended models. Results showed promising potential for different predictive models: where students smoked, having friends who smoked, having people smoke in their presence, chewing tobacco products, and a student's sex significantly predicted their smoking tendencies

Estimating Health Care Costs Among Fragile and Conflict Affected States: an Elastic Net-Risk Measures Approach

Fragile and conflict affected states (FCAS) are those in which the government lacks the political will and/or capacity to provide the basic functions necessary for poverty reduction, economic development, and the security of human rights of their populations.Until recent history, unfortunately, the majority of research conducted and universal health care debates have been centered around middle income and emerging economies. As a result, FCAS have been neglected from many global discussions and decisions. Due to this neglect, many FCAS do not have proper vaccinations and antibiotics. Seemingly, well estimated health care costs are a necessary stepping stone in improving the health of citizens among FCAS. Fortunately, developments in statistical learning theory combined with data obtained by the WBG and Transparency International make it possible to accurately model health care cost among FCAS. The data used in this paper consisted of 35 countries and 89 variables. Of these 89 variables, health care expenditure (HCE) was the only response variable. With 88 predictor variables, there was expected to be multicollinearity, which occurs when multiple variables share relatively large absolute correlation. Since multicollinearity is expected and the number of variables is far greater than the number of observations, this paper adopts Zou and Hastie\u27s method of regularization via elastic net (ENET). In order to accurately estimate the maximum and expected maximum HCE among FCAS, well-known risk measures, such as Value at Risk and Conditional Value at Risk, and related quantities were obtained via Monte Carlo simulations. This paper obtained risk measures at 95 security level

Maternal Mortality in Ghana: Impact of the Fee-Free Delivery Policy and the National Health Insurance Scheme

Maternal mortality (MMR) is the second largest cause of female deaths in Ghana. Yet, many households cannot afford the cost of skilled delivery The study utilized the Panel Data Model to examine the impact of the fee-free delivery (FDP) and the National Health Insurance Policy (NIP) exemptions on MMR in Ghana. The Demographic and Health Survey reports on Ghana from 2002 to 2009 served as the main data source. Data were analyzed using Panel data model with within group fixed effects estimator. MMR declined significantly over the period studied. Both FDP and NIP positively impacted MMR at a 5% level of significance. In addition, skilled delivery was a significant predictor of MMR. Stakeholders would do well to ensure NIP is adequately funded in order to sustain the decline in MMR

Prevalence of Active School Transportation in the Upper East and Upper West Regions of Ghana

The use of active transportation such as walking to and from school is on the decline globally. The primary purpose of the study was to determine the prevalence of active school transportation among primary and junior high school students in the Upper East and Upper West regions of Ghana. The secondary purpose was to examine predictors for meeting the recommended daily number of steps. A total of 2505 (1117 boys and 1388 girls) primary (1583) and junior high school (922) students participated in the study. The distances from children\u27s homes to their schools, heights, and body weights were measured –their heights were used to estimate their stride lengths. The step count for each participant to and from school each day was calculated. Data were analyzed using conditional percentage distribution and Logit model. Analyses indicated that 98.96% of participants used active transportation to and from school. Over 63% of the students were within the normal BMI range. However, 26.47% of the participants were either thin or underweight while 9.9% were either overweight or obese. Overall, 46.47% of the participants met the recommended daily steps. The Logit model indicated that educational level, BMI, mode of transportation, region, height, and age were significant predictors for meeting the recommended daily number of steps. The prevalence of school active transportation in the present study was high compared to that reported in other studies. Furthermore, the prevalence of thinness and underweight were higher than in previous studies, while the prevalence in overweight and obesity were lower

Estimating Health Care Costs among Fragile and Conflict Affected States: An Elastic Net-Risk Measures Approach

Fragile and conflict affected states (FCAS) are those in which the government lacks the political will and/or capacity to provide the basic functions necessary for poverty reduction, economic development, and the security of human rights of their populations.Until recent history, unfortunately, the majority of research conducted and universal health care debates have been centered around middle income and emerging economies. As a result, FCAS have been neglected from many global discussions and decisions. Due to this neglect, many FCAS do not have proper vaccinations and antibiotics. Seemingly, well estimated health care costs are a necessary stepping stone in improving the health of citizens among FCAS. Fortunately, developments in statistical learning theory combined with data obtained by the WBG and Transparency International make it possible to accurately model health care cost among FCAS. The data used in this paper consisted of 35 countries and 89 variables. Of these 89 variables, health care expenditure (HCE) was the only response variable. With 88 predictor variables, there was expected to be multicollinearity, which occurs when multiple variables share relatively large absolute correlation. Since multicollinearity is expected and the number of variables is far greater than the number of observations, this paper adopts Zou and Hastie’s method of regularization via elastic net (ENET). In order to accurately estimate the maximum and expected maximum HCE among FCAS, well-known risk measures, such as Value at Risk and Conditional Value at Risk, and related quantities were obtained via Monte Carlo simulations. This paper obtained risk measures at 95 security level

Teacher Trainees’ Attitudes toward the Untrained Teachers Diploma in Basic Education Program in Ghana

The purpose of the study was to examine teacher trainees’ attitudes toward the Untrained Teachers Diploma in Basic Education Program (UTDBE) in Ghana. Participants were a purposive sample of 284 TTs (139 female; 145 male) enrolled in the residential session of the UTDBE program in two colleges of education. A 21-item 5-point Likert-type questionnaire served as the data source. The predictor variables were year in program, sex, marital status, grade level, and age. Attitude served as the response variable for the study. Overall, only 38.38% of the TTs reported positive attitudes toward the UTDBE. Logit model analyses indicated year in program and grade level were significant predictors of attitude, while sex, marital status and age were not. TTs in their second year were less likely than those in the first year to report positive attitudes toward the UTDBE. Similarly, TTs teaching primary classes were less likely to report positive attitudes toward the program than their colleagues in kindergarten classrooms. Keywords: Attitudes, teacher education, Ghana, untrained teachers

Sequential Extractions and Toxicity Potential of Trace Metals Absorbed into Airborne Particles in an Urban Atmosphere of Southwestern Nigeria

The paper investigates the hypothesis that biotoxicities of trace metals depend not only on the concentration as expressed by the total amount, but also on their geochemical fractions and bioavailability. Airborne particles were collected using SKC Air Check XR 5000 high volume Sampler at a human breathing height of 1.5–2.0 meters, during the dry season months from November 2014 to March 2015 at different locations in Akure (7°10′N and 5°15′E). The geochemical-based sequential extractions were performed on the particles using a series of increasingly stringent solutions selected to extract metals (Cd, Cu, Cr, Ni, Pb, Zn, and Mn) into four operational geochemical phases—exchangeable, reducible, organic, and residual—and then quantified using an Atomic Absorption Spectrophotometer. The results showed metals concentration of order Pb > Cr > Cd > Zn > Ni > Cu > Mn. However, most metals in the samples exist in nonmobile fractions: exchangeable (6.43–16.2%), reducible (32.58–47.39%), organic (4.73–9.88%), and residual (18.28–27.53%). The pollution indices show ingestion as the leading route of metal exposure, with noncarcinogenic (HQ) and cancer risk (HI) for humans in the area being higher than 1.0 × 10−4, indicating a health threat