INTRODUCTION: In the United States, Blacks and Hispanics compared to Whites are disproportionately infected with HIV. Testing for HIV is critical to reduce HIV transmission, lower risk behaviors, and improve access to treatment among persons living with HIV. However, racial & ethnic minorities are tested at later stages of HIV. Previous studies that examined racial discrimination and HIV testing reported inconsistent findings and additional knowledge is needed to understand whether differential treatment based on race is an important barrier to HIV testing. AIM: We examined whether HIV testing is influenced by how an individual reacts to race-based treatment, rather than experiences of discrimination alone, among Whites, Blacks, and Hispanics; and we determined if this relationship was modified race and ethnicity. METHODS: We performed a cross-sectional analysis of the 2012 Behavioral Risk Factor Surveillance System’s (n=12,579) self-reported HIV testing data and Reaction to Race (RR) module, which captures experiences of differential treatment based on race and an individual’s reaction to racialized treatment. Multivariable logistic regression was used to assess the association between RR-based treatment and HIV testing. Statistical interaction between RR-based treatment and race was assessed. RESULTS: Approximately 21% participants reported ever being tested for HIV, and 19% of the participants had one or more experiences of RR-based treatment. Prevalence of HIV testing was higher among Blacks (62%) and Hispanics (33%) compared to Whites (32%). In an adjusted model, the odds of HIV testing among those who reported one experience of Reactions to Race based treatment was 1.37 (95% CI: 1.08-1.75) times the odds among those with no experiences of RR-based treatment. We did not detect statistical interaction between RR-based treatment and HIV testing by race. DISCUSSION: Our findings suggest that experiences of racial discrimination may be counter intuitively associated with increased HIV testing overall and within each racial and ethnic group. Additional research is needed to clarify settings in which experiences of race-based treatment and the associated reactions to the treatment can positively or negatively influence HIV testing behaviors.

In healthcare-related studies, individual patient or hospital data are not often publicly available due to privacy restrictions, legal issues or reporting norms. However, such measures may be provided at a higher or more aggregated level, such as state-level, county-level summaries or averages over health zones such as Hospital Referral Regions (HRR) or Hospital Service Areas (HSA). Such levels constitute partitions over the underlying individual level data, which may not match the groupings that would have been obtained if one clustered the data based on individual-level attributes. Moreover, treating aggregated values as representatives for the individuals can result in the ecological fallacy. How can one run data mining procedures on such data where different variables are available at different levels of aggregation or granularity? In this thesis, we seek a better utilization of variably aggregated datasets, which are possibly assembled from different sources. We propose a novel "cross-level" imputation technique that models the generative process of such datasets using a Bayesian directed graphical model. The imputation is based on the underlying data distribution and is shown to be unbiased. This imputation can be further utilized in a subsequent predictive modeling, yielding improved accuracies. The experimental results using a simulated dataset and the Behavioral Risk Factor Surveillance System (BRFSS) dataset are provided to illustrate the generality and capabilities of the proposed framework. / text

The growing prevalence of overweight and obesity among United States (U.S.) elderly people today is a health concern. Higher incidences of obesity and obesity-related health conditions and mortality exist in the southern area of the U.S. Understanding obesity in relation to obesity-related factors in this population is crucial. The purpose of this study was to identify regional differences and associations between obesity and obesity-related factors in Southern U.S. elderly people, as defined by the U.S. Census Bureau, using data from the 2005 Behavioral Risk Factor Surveillance System (BRFSS), an existing telephone health survey administered by the Centers for Disease Control and Prevention (CDC). Through frequency tests, chi-square tests, and a multinomial logistic regression, the results revealed no regional difference in weight status among U.S. elderly people. However, multinomial logistic regression indicated some consistent associations with weight status among Southern U.S. elderly people. Males, Blacks and married elderly people, along with those diagnosed with high cholesterol, diabetes, and hypertension were associated with both overweight and obesity. Associations found between Southern U.S. elderly people who were overweight or obese and obesity-related factors support the need to continue to encourage elderly people living in the South to control their weight.

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The main purpose of this study is to examine the differences of preventive management utilizations and diabetes complications in Asian Americans and Non-Hispanic whites using multiple years (2002-2013) Behavioral Risk Factor Surveillance System (BRFSS). SAS for complex survey procedures were used to perform the data analysis. Odds ratios (OR) were calculated to compare the prevalence of diabetes complications and preventive management rate in Asian with white. Compared to white, the prevalence of diabetes retinopathy in Asians were higher, while the rates of neuropathy and cardiovascular complications, pneumonia shot, personally management as well as management diabetes with doctors were lower. The prevalence of routine checkup in Asian was not significantly different from the prevalence in white. More attentions should be paid on Asians for diabetes related retinopathy.

Background: Arthritis interferes with quality of life, results in enormous medical and social costs, and is the leading cause of disability in the United States. Overweight and obesity have been found to be associated with specific types of arthritis, but the relationship between excess body weight and arthritis in general has not been well characterized at the population level. Furthermore, previous studies failed to utilize the CDC validated surveillance case definition of arthritis. Objectives: To examine the association between body mass index (BMI: kg/m2) and arthritis using the CDC validated surveillance case definition of arthritis and to describe the prevalence of arthritis across population subgroups based on body mass index and other select characteristics. Methods: Cross-sectional data from the 2005 Behavioral Risk Factor Surveillance System survey were analyzed. Using population weights, descriptive statistics and prevalences were generated. Univariate and multivariate analyses with 95% confidence intervals (CI) were conducted to examine the risk estimates (odds ratios/ORs) and to assess the relationship between body mass index and arthritis among U.S. adults, (N=356,112). SAS 9.1 software was used for all analyses.Results: Overall, 26% of US adults had self-reported arthritis. Obese persons (BMI: >30) were 2 times more likely to report arthritis compared to normal weight respondents, (BMI: 40): OR= 3.1, 95%CI= 2.9, 3.4; Class II Obesity, (BMI: 35-39.9): OR=2.4, 95% CI= 2.3, 2.6; Class I Obesity, (BMI: 30-34.9): OR= 2.0, 95% CI= 1.9, 2.1] The association between the BMI groups and arthritis did not change significantly after taking demographic and socioeconomic variables into account. Older age, female gender, higher income, and lack of any physical activity were associated with a higher odds of reporting arthritis, while insurance status and being non-White were not.Conclusions: BMI is an important independent risk factor for self-reported arthritis. Resources must be allocated to prevent and reduce weight gain in the population, especially among women and younger adults.

Background: Despite the growing evidence of the positive effects of fruit consumption on health, many individuals do not consume the recommended dietary guideline amounts. It has been suggested that socioeconomic status and income have an influence on food choices and consumption. The aim of this study is not only to examine whether payment structure has an association with food choices but also to assess fruit consumption independent of vegetables in the US. Methods: The 2011 Behavioral Risk Factor Surveillance System was utilized and the study design led to a sample size that was n= 19,122 respondents. Variables that were selected for associations with sufficient fruit consumption included demographic data, employment status, payment structure, education, and home ownership status. A p-value of <0.05 and 95% confidence intervals were used to determine statistical significance of the analyses performed. Results: Factors that were associated with greater odds of sufficient fruit consumption included being African-American, education- all levels of high school graduate and higher, all income categories above $15,000 annually, those employed, and those who rent a home (p-value<0.01). Multivariate logistic regression analysis indicated that respondents' education defined as having college education was associated with increased odds of sufficient fruit consumption (OR = 7.09: CI =1.86-27.09] (p-value<0.01). Conclusions: Assessing fruit consumption alone did not provide greater insight on sufficiency with the exception of race's (specifically African American) influence. Payment structure was found not associated with increased fruit consumption. Promotion of education on the relevance of fruit consumption to overall health is critical and necessary in the United States.

Behavior risk factors include health risk factors that increase a person's chances of developing a disease, such as having a high blood pressure, high blood cholesterol, tobacco smoke, physical inactivity, obesity or overweight, diabetes, poor nutrition, lack of sex education and car safety. They can be classified as: Background risk factors, such as age, sex, level of education and genetic compositions; Behavioral risk factors, such as smoking, unhealthy diet and physical inactivity; and Intermediate risk factors, such a serum cholesterol levels, diabetes, hypertension and obesity/overweight. This study describes a comparison and assessment of Behavior Risk Factor Surveillance Systems for the U.S., Canada, and Italy. The aim of this project is to assess and analyze the behavior surveillance systems of U.S., Canada and Italy, compare their strengths and weaknesses and provide recommendations that can be used as a guide for the design of new BRFS systems or the assessment of existing systems. The purpose of the assessment is to identify ways of improving the respective systems, and also to compare public health BRFS systems in the three different countries. The attributes used in the evaluation of the systems include simplicity, flexibility, data quality, acceptability, sensitivity, predictive value positive, representativeness, timeliness, and stability. The criteria and standards are based on the CDC Guidelines for Evaluating Surveillance Systems published on 1988 and updated on 2001.

OBJECTIVE: To examine associations and trends between chronic diseases and tooth loss using BRFSS 2012-2018. METHODS: Self-reported permanent tooth loss from tooth decay/gum disease and several self-reported chronic disease diagnoses were analyzed by cycle (2012, 2014, 2016, 2018) to explore associations and trends. Chi-square analyses were performed for the primary outcome of one or more teeth lost with the following ailments: physical health, mental health, weight, diabetes, myocardial infarction, coronary heart disease, stroke, asthma, cancer, respiratory diseases, arthritis, and kidney disease. Multivariate logistic regressions were performed to estimate the odds for tooth-loss for each disease using gender, age, race, insurance, income, education and smoking as covariates. Effects of one or more concurrent chronic disease diagnoses on tooth loss were calculated and 2012-2018 results compared. Interaction between disease and year were used in the multivariate regression aanalyses to find differences in tooth loss from 2012- 2018. All calculations were performed using SAS 9.4. RESULTS: Tooth loss has declined from 45% - in 2012 to 39% - in 2018 in individuals with one chronic disease. A similar decline in tooth loss is seen in those with two, three, four or more chronic diseases. Increased tooth-loss was significantly associated with each chronic disease, with adjusted odds of tooth-loss ranging from 1.08-1.72. Diabetics, had an increased and significant odds of tooth loss with time: 1.36 (2012)-1.54 (2018). The odds of tooth-loss increased as number of concurrent chronic diseases increased -1.2 (one chronic disease)-2.4 (four or more chronic diseases). CONCLUSION: Fewer people are losing teeth, but those with chronic disease experience higher odds of tooth-loss. Having more concurrent diseases is associated with increased tooth-loss. Oral health is essential for overall health, therefore access to oral health care and educating the public and health professionals about these associations is vital. / 2021-07-29T00:00:00Z

Background: Obesity remains a major public health problem and a risk factor for developing chronic diseases. Substance use such as e-cigarette, marijuana, and alcohol have been associated with the risk of being obese. However, the results from previous studies have been inconsistent. The purpose of this study is to determine the association between substance use and obesity among adults in the United States. Method: Data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS), an annual cross-sectional survey administered to 446,687 adults in all 50 states to collect information about their health-related risk behaviors, chronic health conditions and the use of preventive services, was used in this study. Data was collected via a self-reported questionnaire validated by CDC. A multiple logistic regression model was conducted to determine the association between exposure variables (e-cigarette, marijuana, and alcohol abuse) and obesity. The model was adjusted for possible confounders such as demographics (age, sex and race) and behaviors such as tobacco smoking and physical activity. The data was analyzed using SAS v 9.4. Results: Individuals who used marijuana during the past 30 days were 32.4% less likely (adjusted odds ratio (aOR): 0.676, 95% CI: 0.631-0.723, P<0.001) to be obese compared to those who did not. The odds of being obese among heavy alcohol drinkers was 30% less (aOR: 0.70, 95% CI: 0.679-0.721, p<0.001) compared to those who were not heavy alcohol drinkers. Conclusion: The study findings demonstrate that marijuana and heavy alcohol drinking are significantly associated with reduced likelihood of obesity. However, e-cigarette use was not significantly associated with obesity. Further longitudinal studies to explore the relationship between these substances and obesity will be beneficial.