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Background Prostate cancer (CaP) cases are high in the United States. According to the American Cancer Society, there are an estimated number of 174,650 CaP new cases in 2019. The estimated number of deaths from CaP in 2019 is 31,620, making CaP the second leading cause of cancer deaths among American men with lung cancer been the first. Our goal is to estimate and map prostate cancer relative risk, with the ultimate goal of identifying counties at higher risk where interventions and further research can be targeted. Methods The 2012–2016 Surveillance, Epidemiology, and End Results (SEER) Program data was used in this study. Analyses were conducted on 159 Georgia counties. The outcome variable is incident prostate cancer. We employed a Bayesian geospatial model to investigate both measured and unmeasured spatial risk factors for prostate cancer. We visualised the risk of prostate cancer by mapping the predicted relative risk and exceedance probabilities. We finally developed interactive web-based maps to guide optimal policy formulation and intervention strategies. Results Number of persons above age 65 years and below poverty, higher median family income, number of foreign born and unemployed were risk factors independently associated with prostate cancer risk in the non-spatial model. Except for the number of foreign born, all these risk factors were also significant in the spatial model with the same direction of effects. Substantial geographical variations in prostate cancer incidence were found in the study. The predicted mean relative risk was 1.20 with a range of 0.53 to 2.92. Individuals residing in Towns, Clay, Union, Putnam, Quitman, and Greene counties were at increased risk of prostate cancer incidence while those residing in Chattahoochee were at the lowest risk of prostate cancer incidence. Conclusion Our results can be used as an effective tool in the identification of counties that require targeted interventions and further research by program managers and policy makers as part of an overall strategy in reducing the prostate cancer burden in Georgia State and the United States as a whole.

Background In clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific trajectories of biomarkers over time. Despite their increasing popularity and application, the specification of these models necessitates a great deal of care when analysing longitudinal data with non-linear patterns and asymmetry. Parametric (linear) mixed-effect models may not capture these complexities flexibly and adequately. Additionally, assuming a Gaussian distribution for random effects and/or model errors may be overly restrictive, as it lacks robustness against deviations from symmetry. Methods This paper presents a semiparametric mixed-effects model with flexible distributions for complex longitudinal data in the Bayesian paradigm. The non-linear time effect on the longitudinal response was modelled using a spline approach. The multivariate skew-t distribution, which is a more flexible distribution, is utilized to relax the normality assumptions associated with both random-effects and model errors. Results To assess the effectiveness of the proposed methods in various model settings, simulation studies were conducted. We then applied these models on chronic kidney disease (CKD) data and assessed the relationship between covariates and estimated glomerular filtration rate (eGFR). First, we compared the proposed semiparametric partially linear mixed-effect (SPPLM) model with the fully parametric one (FPLM), and the results indicated that the SPPLM model outperformed the FPLM model. We then further compared four different SPPLM models, each assuming different distributions for the random effects and model errors. The model with a skew-t distribution exhibited a superior fit to the CKD data compared to the Gaussian model. The findings from the application revealed that hypertension, diabetes, and follow-up time had a substantial association with kidney function, specifically leading to a decrease in GFR estimates. Conclusions The application and simulation studies have demonstrated that our work has made a significant contribution towards a more robust and adaptable methodology for modeling intricate longitudinal data. We achieved this by proposing a semiparametric Bayesian modeling approach with a spline smoothing function and a skew-t distribution.

Obesity is among the most common global public health issues in the 21 century and contributes significantly to cardiovascular morbidity and mortality burden. The success of well-targeted policies and intervention strategies aimed at addressing obesity depends heavily on understanding the effect of geographical location on obesity and other predictors. The study aim was to quantify county-level geographical differences in obesity across Florida counties while simultaneously identifying predictors of obesity prevalence. This study used the 2019 data from the Florida state-based telephone surveillance systems, known as the Behavioral Risk Factor Surveillance System (BRFSS) which provides county-level data on measures of the prevalence of personal health behaviors that are risk factors for morbidity and mortality. The survey collected data on a total sample of 54,260 adults residing in 67 counties of Florida. This study applied Bayesian geospatial models and interactive web-based mapping approaches to analyze and map county-level geographical differences in the risk of obesity. The estimated coefficients were presented as log mean with their associated 95% credible intervals (Cr.Is). The study identified sedentary lifestyle (log mean = 0.023, 95% Cr.I: 0.006, 0.039) as the only risk factor independently associated with increased burden of obesity. The results showed substantial county-level geographical differences in the predicted obesity prevalence with an overall obesity prevalence of 68.6% with a range of 59.0%-75.7%. Residing in Holmes was associated with the highest burden of obesity. Furthermore, the prevalence was relatively high in Levy, Columbia, Lafayette, Hendry, Bradford, Calhoun, Dixie, Okeechobee, and Gadsden counties. The substantial county-level geographical difference in obesity prevalence found is of great importance for sound public health policy and intervention strategies at the local level. The geospatial modeling supported by the web-based spatial mapping tool employed in this study can help guide the design of geographical prioritization of targeted public health policies and intervention strategies to combat adult obesity and its associated mortality.

PurposeExamining spatial distribution of colorectal cancer (CRC) incidence or mortality is helpful for developing cancer control and prevention programs or for generating hypotheses. Such an investigation involves describing the spatial variation of risk factors for CRC and identifying hotspots. The aim of this study is to identify county-level risk factors that may be associated with the incidence of CRC and to map hotspots for CRC in Florida.MethodsCounty-level CRC cases, recorded in 2018, were obtained from the Florida Department of Health, Division of Public Health Statistics & Performance Management (DPHSM). Data on county-level risk factors were also obtained from the same source. We used Bayesian spatial models for relative incidence rates and produced posterior predictive that indicates excess risk (hotspots) for CRC.ResultsThe county-level unadjusted incidence rates range from .462 to 3.142. After fitting a Bayesian spatial model to the data, the results show that a decreasing risk of CRC is strongly associated with an increasing median income, higher percentage of Black population, and higher percentage of sedentary life at county level. Using exceedance probability, it is also observed that there are clustering and hotspots of high CRC incidence rates in Charlotte County in South Florida, Hernando, Sumter and Seminole counties in central Florida and Union and Washington counties in north Florida.ConclusionAmong few county-level variables that significantly explained the spatial variation of CRC, income disparity may need more attention for resource allocation and developing preventive intervention in high-risk areas for CRC.

Background and aims Hypertension is a major public health issue, an important risk factor for cardiovascular diseases and stroke, especially in developing countries where the rates remain unacceptably high. In Africa, hypertension is the leading driver of cardiovascular disease and stroke deaths. Identification of critical risk factors of hypertension can help formulate targeted public health programs and policies aimed at reducing the prevalence and its associated morbidity, disability, and mortality. This study attempts to develop multilevel regression, an in‐depth statistical model to identify critical risk factors of hypertension. Methods This study used data on 4667 individuals aged ≥18 years from the nationally representative World Health Organization Study on global AGEing and adult health (SAGE) Ghana Wave 2 conducted in 2014/2015. Multilevel regression modeling was employed to identify critical risk factors for hypertension based on systolic blood pressure (SBP) (ie, SBP > 140 mmHg). Of the 4667, 27.3% were hypertensive. Final data on 4381 individuals residing in 3790 households were analyzed using multilevel models, and results were presented as adjusted odds ratios (aOR) and their associated 95% confidence intervals (CI). Results Risk factors for hypertension identified were age (aOR) = 5.4, 95% CI: 4.11‐7.09), obesity (aOR = 1.51, 95% CI: 1.19‐1.91), marital status (aOR = 0.75, 95% CI: 0.64‐0.89), perceived health state (moderate; aOR = 1.38, 95% CI: 1.15‐1.65 and bad/very bad; aOR = 1.35, 95% CI: 1.0‐1.83), and difficulty with self‐care (aOR = 1.64, 95% CI: 1.1‐2.44). We found unobserved significant differences in the likelihood of hypertension prevalence between different households. Conclusion Addressing the problem of obesity, targeting specific interventions to those aged over 50 years, and improvement in the general health of Ghanaians are paramount to reducing the prevalence and its associated morbidity, disability, and mortality. Lifestyle modification in the form of dietary intake, knowledge provision supported with strong public health message, and political will could be beneficial to the management and prevention of hypertension.

Background A mosquito repellent has the potential to prevent malaria infection, but there has been few studies demonstrating the effectiveness of combining this strategy with the highly effective long-lasting insecticidal nets (LLINs). This study aimed to determine the effect of combining community-based mosquito repellent with LLINs in the reduction of malaria. Methods A community-based clustered-randomised trial was conducted in 16 rural villages with 1,235 households in southern Ethiopia between September and December of 2008. The villages were randomly assigned to intervention (mosquito repellent and LLINs, eight villages) and control (LLINs alone, eight villages) groups. Households in the intervention villages received mosquito repellent (i.e., Buzz-Off® petroleum jelly, essential oil blend) applied every evening. The baseline survey was followed by two follow-up surveys, at one month interval. The primary outcome was detection of Plasmodium falciparum , Plasmodium vivax , or both parasites, through microscopic examination of blood slides. Analysis was by intention to treat. Baseline imbalances and clustering at individual, household and village levels were adjusted using a generalized linear mixed model. Results 3,078 individuals in intervention and 3,004 in control group were enrolled into the study. Compared with the control arm, the combined use of mosquito repellent and LLINs significantly reduced malaria infection of all types over time [adjusted Odds Ratio (aOR) = 0.66; 95% CI = 0.45-0.97]. Similarly, a substantial reduction in P. falciparum malaria infection during the follow-up surveys was observed in the intervention group (aOR = 0.53, 95% CI = 0.31-0.89). The protective efficacy of using mosquito repellent and LLINs against malaria infection of both P. falciparum/P. vivax and P. falciparum was 34% and 47%, respectively. Conclusions Daily application of mosquito repellent during the evening followed by the use of LLINs during bedtime at community level has significantly reduced malaria infection. The finding has strong implication particularly in areas where malaria vectors feed mainly in the evening before bedtime. Trial registration ClinicalTrials.gov identifier: NCT01160809 .

This paper presents a Bayesian quantile bent-cable growth model for modeling data with multi-phasic trajectories of response variables measured in longitudinal studies. Estimating and identifying such possible multiple phasic change points may be of substantial interest since it provides a general life-course view of the developmental trajectories and also when the directions of the trajectories are disrupted. For getting a complete picture of such developmental trajectories, quantile growth models, at different parts (quantiles) of the response variables, are better than the commonly used conditional mean models. For each quantile, we develop bent-cable models to assess multi-phasic patterns of trajectories of longitudinal HIV/AIDS data with left-censoring and skewness. The proposed procedures are illustrated using real data from an AIDS clinical study.

Purpose We examined colorectal cancer (CRC) risk perceptions among Black men in relation to socio-demographic characteristics, disease prevention factors, and personal/family history of CRC. Methods A self-administered cross-sectional survey was conducted in five major cities in Florida between April 2008 and October 2009. Descriptive statistics and multivariable logistic regression were performed. Results Among 331 eligible men, we found a higher proportion of CRC risk perceptions were exhibited among those aged ≥ 60 years (70.5%) and American nativity (59.1%). Multivariable analyses found men aged ≥ 60 had three times greater odds of having higher CRC risk perceptions compared to those ≤ 49 years (95% CI = 1.51–9.19). The odds of higher CRC risk perception for obese participants were more than four times (95% CI = 1.66–10.00) and overweight were more than twice the odds (95% CI = 1.03–6.31) as compared to healthy weight/underweight participants. Men using the Internet to search for health information also had greater odds of having higher CRC risk perceptions (95% CI = 1.02–4.00). Finally, men with a personal/family history of CRC were ninefold more likely to have higher CRC risk perceptions (95% CI = 2.02–41.79). Conclusion Higher CRC risk perceptions were associated with older age, being obese/overweight, using the Internet as a health information source, and having a personal/family history of CRC. Culturally resonate health promotion interventions are sorely needed to elevate CRC risk perceptions for increasing intention to screen among Black men.

Studies of human immunodeficiency virus dynamics in acquired immuno deficiency syndrome (AIDS) research are very important in evaluating the effectiveness of antiretroviral (ARV) therapies. The potency of ARV agents in AIDS clinical trials can be assessed on the basis of a viral response such as viral decay rate or viral load change in plasma. Following ARV treatment, the profile of each subject's viral load tends to follow a ‘broken stick’-like dynamic trajectory, indicating multiple phases of decline and increase in viral loads. Such multiple-phases (change-points) can be described by a random change-point model with random subject-specific parameters. One usually assumes a normal distribution for model error. However, this assumption may be unrealistic, obscuring important features of within- and among-subject variations. In this article, we propose piecewise linear mixed-effects models with skew-elliptical distributions to describe the time trend of a response variable under a Bayesian framework. This methodology can be widely applied to real problems for longitudinal studies. A real data analysis, using viral load data from an AIDS study, is carried out to illustrate the proposed method by comparing various candidate models. Biologically important findings are reported, and these findings also suggest that it is very important to assume a model with skew distribution in order to achieve reliable results, in particular, when the data exhibit skewness.

This paper presents an extension of the standard Tobit to simultaneously address segmental phases, subpopulation heterogeneity, lower limit of detection, and skewness in outcomes of human immunodeficiency virus (HIV) or acquired immunodeficiency syndrome (AIDS) longitudinal data. A major problem often encountered in an HIV/AIDS research is the development of drug resistance to antiretroviral (ARV) drug or therapy. For dealing with drug resistance problem, estimating the time at which drug resistance would develop is usually sought. Following ARV treatment, the profile of each subject’s viral load tends to follow a ‘broken stick’ like growth trajectory, indicating multiple phases of decline and increase in viral loads. Such multiple phases with multiple change-points are captured by subject-specific random parameters of growth curve models. To account subpopulation heterogeneity of drug resistance among patients, the turning-points are also allowed to differ by latent classes of patients on the basis of trajectories of observed viral loads. These features of viral longitudinal data are jointly modeled in a unified framework of segmental growth mixture Tobit mixed-effects models with skew distributions for a response variable with left censoring and skewness under the Bayesian approach. The proposed methods are illustrated using real data from an AIDS clinical study.