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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.

BackgroundUpfront surgery is the current standard for resectable intrahepatic cholangiocarcinoma (ICC) despite high treatment failure with this approach. We sought to examine the use of neoadjuvant chemotherapy (NAC) as an alternative strategy for this population.MethodsThe National Cancer Database was used to identify patients with resectable ICC undergoing curative-intent surgery (2006–2014). Utilization trends were examined and survival estimates between NAC and upfront surgery were compared; propensity score-matched models were used to examine the association of NAC with overall survival (OS) for all patients and risk-stratified cohorts. Models accounted for clustering within hospitals, and results represent findings from a complete-case analysis.ResultsAmong 881 patients with ICC, 8.3% received NAC, with no changes over time (Cochran–Armitage p = 0.7). Median follow-up was 50.9 months, with no difference in unadjusted survival with NAC versus upfront surgery (median OS 51.8 vs. 35.6 months, and 5-year OS rates of 38.2% vs. 36.6%; log rank p = 0.51), and no survival benefit in the propensity score-matched analysis (hazard ratio [HR] 0.78, 95% CI 0.54–1.11; p = 0.16). However, for patients with stage II–III disease, NAC was associated with a trend towards improved survival (median OS of 47.6 months vs. 25.9 months, and 5-year OS rates of 34% vs. 25.7%; log-rank p = 0.10) and a statistically significant survival benefit in the propensity score-matched analysis. (HR 0.58, 95% CI 0.37–0.91; p = 0.02).ConclusionNAC is associated with improved OS over upfront surgery in patients with resectable ICC and high-risk of treatment failure. These data support the need for prospective studies to examine NAC as an alternative strategy to improve OS in this population.

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.

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.

Background The past two decades has seen a steady rise in the number of immigrants from sub-Saharan Africa to the United States (US). While this population represent one of the fastest growing segments of the US immigrant population, research examining the impact of migration on the health practices and behaviors among this population has lagged. Acculturation is the modification of an individual’s beliefs, values, and behavior upon contact with a different culture. This study aimed to enhance understanding of the links between acculturation and health practices among sub-Saharan Africa immigrants in the US. Methods Constructivist Grounded Theory methodology was employed to develop a process model that explains the relationship between acculturation and health behaviors of sub-Saharan African immigrant men. This qualitative approach included in-depth interviews with 36 participants and a follow-up interview with 10 participants. Purposive sampling was used to recruit participants and audio recordings of interviews were transcribed, verified, and analyzed using an iterative constant comparative approach that comprised open, axial, and focused coding. Emerging constructs were identified and synthesized into a process model which was further refined with input from an advisory panel and participant checking. Results The Model of Acculturation and Health Practices among sub-Saharan African immigrant men (MAPS) in the US can be categorized into four main pillars: inputs, contexts, mechanisms, and outcomes. Inputs conceptualize pre-immigration experience including reasons for migration. Contextual factors describe prevailing conditions in the US including the political and economic aspects surrounding immigration. Mechanisms depict how acculturation impacts health behaviors. Outcomes illustrate the proximal and distal outcomes. Conclusion This Model of Acculturation and Health Practices among sub-Saharan African immigrant men identifies key constructs that should be considered when studying acculturation and health practices among sub-Saharan Africa immigrants in the US. Notably, MAPS can aid in providing healthcare providers with a comprehensive lens through which to understand the complexities of sub-Saharan African Immigrant men’s health behaviors. This study fills the void of dearth in theoretical models that elucidate behavioral adaptation and preventive behavior among sub-Saharan Africa immigrants in the US.

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 Transcranial magnetic stimulation (TMS) is an established treatment for major depressive disorder (MDD), yet response rates remain suboptimal and biomarkers predictive of treatment outcomes are currently lacking. Recently, DNA methylation (DNAm) has shown promise as an epigenetic predictor of antidepressant and electroconvulsive therapy treatment outcomes but no study to our knowledge has characterized DNAm profiles of treatment outcomes in the context of TMS. Here, we present the first genome-scale DNAm analysis of TMS outcomes in patients with treatment-resistant depression (TRD). Methods Peripheral blood samples from 60 TRD patients were collected prior to a standard 36-session TMS course. DNAm was profiled using the Illumina EPIC array and filtered to retain only the top 5% most variable probes for subsequent analysis in relation to three treatment outcomes in an analytic sample of 46 patients: treatment response (> 50% PHQ-9 reduction), symptom trajectory (ΔPHQ-9), and remission (PHQ-9 < 5). Results Methylated CpG Set Enrichment Analysis ( mCSEA ) identified 67, 23, and 163 differentially methylated regions (DMRs) associated with treatment response, symptom trajectory, and remission, respectively (FDR < 0.05). Sixteen DMRs were common across all outcomes, implicating genes involved in neurodevelopment ( HOXA4 , HOXA5 ), immune signaling ( RUNX1 , OTUD5 ), and synaptic function ( EFNB1 , RAP2C). Targeted analysis of 84 CpGs in the BDNF promoter revealed 10 nominally significant CpGs that differentiated responders from non-responders. Several DMRs showed strong blood–brain methylation concordance ( r  > 0.5), supporting their potential relevance to central nervous system mechanisms. Conclusions Despite the limited sample size, these findings suggest distinct epigenetic signatures prior to treatment that are associated with TMS outcomes, supporting the potential utility of DNAm as a biomarker for response stratification in TRD.

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.