Gender differences in prevalence of myocardial infarction in rural West Texans.

Background: Heart disease is the leading cause of death in the United States. Incidence rates of myocardial infarction (MI) in rural West Texas signify a lack of effective, risk-specific prevention programs. The purpose of this study was to identify gender-specific risk factors for MI in rural West Texans. Subjects and methods: Hospital patient data for those with and without a history of MI were obtained from the Project FRONTIER database for rural West Texas counties. We used statistical software, such as SPSS, R, and WinBUGS to detect and understand the nature of MI risk factors. Statistical methods including t-tests, Chi-squared, logistic regression, and a Bayesian approach were utilized to analyze data. Results: MI significant risk factors obtained for females were systolic blood pressure (p = 0.002), diastolic blood pressure (p = 0.004), pulse (p = 0.015), and smoking (p = 0.002). For males, these were glucose (p = 0.022), age (p = 0.050), body fat (p = 0.034), and smoking (p = 0.017). The mean risk parameter followed a normal distribution, while the precision parameter depicted skew for both sexes. Conclusions: Gender-specific differences in MI risk factors exist, and incorporating such variables can guide relevant policymaking to reduce MI incidence in rural West Texans.

Colorectal cancer screening use among insured adults: Is out-of-pocket cost a barrier to routine screening?

AIM: To describe the characteristics of adults who needed to see a doctor in the past year but could not due to the extra cost and assess the impact of limited financial resources on the receipt of routine fecal occult blood test, sigmoidoscopy, or colonoscopy for colon cancer screening among insured patients. METHODS: Data obtained from the 2012 Behavioral Risk Factor Surveillance System included 215436 insured adults age 50-75 years. We computed frequencies, adjusted odds ratios (aORs), and 95%CIs using SAS v9.3 software. RESULTS: Nine percent of the study population needed to see a doctor in the past year but could not because of cost. The numbers were significantly higher among those aged 50-64 (P < 0.0001), Non-Hispanic Whites (P < 0.0001), and those with a primary care physician (P < 0.0001) among other factors. Adjusting for possible confounders, aORs for not seeing the doctor in the past year because of cost were: stool occult blood test within last year aOR = 0.88; 95%CI: 0.76-1.02, sigmoidoscopy within last year aOR = 0.72; 95%CI: 0.48-1.07, colonoscopy within the last year aOR = 0.91; 95%CI: 0.81-1.02. CONCLUSION: We found that the limited financial resources within the past 12 mo were significantly associated with colorectal cancer (CRC) non-screening. Patients with risk factors identified in this study should adhere to CRC guidelines and should receive financial help if needed.

Does Breast Cancer Drive the Building of Survival Probability Models among States? An Assessment of Goodness of Fit for Patient Data from SEER Registries

Background: Breast cancer is a worldwide public health concern and is the most prevalent type of cancer in women in the United States. This study concerned the best fit of statistical probability models on the basis of survival times for nine state cancer registries: California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, and Washington. Materials and Methods: A probability random sampling method was applied to select and extract records of 2,000 breast cancer patients from the Surveillance Epidemiology and End Results (SEER) database for each of the nine state cancer registries used in this study. EasyFit software was utilized to identify the best probability models by using goodness of fit tests, and to estimate parameters for various statistical probability distributions that fit survival data. Results: Statistical analysis for the summary of statistics is reported for each of the states for the years 1973 to 2012. Kolmogorov-Smirnov, Anderson-Darling, and Chi-squared goodness of fit test values were used for survival data, the highest values of goodness of fit statistics being considered indicative of the best fit survival model for each state. Conclusions: It was found that California, Connecticut, Georgia, Iowa, New Mexico, and Washington followed the Burr probability distribution, while the Dagum probability distribution gave the best fit for Michigan and Utah, and Hawaii followed the Gamma probability distribution. These findings highlight differences between states through selected sociodemographic variables and also demonstrate probability modeling differences in breast cancer survival times. The results of this study can be used to guide healthcare providers and researchers for further investigations into social and environmental factors in order to reduce the occurrence of and mortality due to breast cancer.