Assessing the Risk of Heart Attack: A Bayesian Kernel Machine Regression Analysis of Heavy Metal Mixtures.

Background: The assessment of heavy metals' effects on human health is frequently limited to investigating one metal or a group of related metals. The effect of heavy metals mixture on heart attack is unknown. Methods: This study applied the Bayesian kernel machine regression model (BKMR) to the 2011-2016 National Health and Nutrition Examination Survey (NHANES) data to investigate the association between heavy metal mixture exposure with heart attack. 2972 participants over the age of 20 were included in the study. Results: Results indicate that heart attack patients have higher levels of cadmium and lead in the blood and cadmium, cobalt, and tin in the urine, while having lower levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine. The estimated risk of heart attack showed a negative association of 0.0030 units when all the metals were at their 25 th percentile compared to their 50 th percentile and a positive association of 0.0285 units when all the metals were at their 75 th percentile compared to their 50 th percentile. The results suggest that heavy metal exposure, especially cadmium and lead, may increase the risk of heart attacks. Conclusions: This study suggests a possible association between heavy metal mixture exposure and heart attack and, additionally, demonstrates how the BKMR model can be used to investigate new combinations of exposures in future studies.

Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data.

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.

Unplanned hysterectomy: a comprehensive analysis of race, ethnicity, sociodemographic factors, pregnancy complications, and cardiovascular disease risk factors.

OBJECTIVE: To understand the risk of unplanned hysterectomy (UH) in pregnant women better in association with maternal sociodemographic characteristics, cardiovascular disease (CVD) risk factors, and current pregnancy complications. DESIGN: Using Florida birth data from 2005 to 2014, we investigated the possible interactions between known risk factors of having UH, including maternal sociodemographic characteristics, maternal medical history, and other pregnancy complications. Logistic regression models were constructed. Adjusted odds ratios and 95% confidence intervals were reported. RESULTS: Several interactions were observed that significantly affected odds of UH. Compared to non-Hispanic White women, Hispanic minority women were more likely to have an UH. The overall risk of UH for women with preterm birth (<37 weeks) and concurrently had premature rupture of membranes (PRoM), uterine rupture, or a previous cesarean delivery was significantly higher than women who delivered to term and had no pregnancy complications. Women who delivered via cesarean who also had preeclampsia, PRoM, or uterine rupture had an overall increased risk of UH. Significantly decreased risk of UH was seen for Black women less than 20 years old, women of other minority races with either less than a high school degree or a college degree or greater, women of other minority races with PRoM, and women with preterm birth and diabetes compared to respective reference groups. CONCLUSIONS: Maternal race, ethnicity, CVD risk factors, and current pregnancy complications affect the risk of UH in pregnant women through complex interactions that would not be seen in unadjusted models of risk analysis.

Spatial mapping of colorectal cancer screening uptake and associated factors.

OBJECTIVE: Over the past decades, it has been understood that the availability of screening tests has contributed to a steady decline in incidence of colorectal cancer (CRC). However, it is also seen that there is a geographic disparity in the use of such tests across small areas. The aim of this study is to examine small-area level barrier factors that may impact CRC screening uptake and to delineate coldspot (low uptake of screening) counties in Florida. METHODS: Data on the percentages of county-level CRC screening uptakes in 2016 and county-level barrier factors for screening were obtained from the Florida Department of Health, Division of Public Health Statistics & Performance Management. Bayesian spatial beta models were used to produce posterior probability of deceedance to identify coldspots for CRC screening rates. RESULTS: Unadjusted screening rates using sigmoidoscopy or colonoscopy test ranged from 56.8 to 85%. Bayesian spatial beta models were fitted to the proportion data. At an ecological level, we found that an increasing rate of CRC screening uptake for either of the test types (colon/rectum exam, stool-based test) was strongly associated with a higher health insurance coverage, and lower percentage of population that speak English less than very well (immigration) at county level. Eleven coldspot counties out of 67 total were also identified. CONCLUSION: This study suggests that health insurance disparities in the use of CRC screening tests are an important factor that may need more attention for resource allocation and health policy targeting small areas with low uptake of screening.

Colorectal Cancer Risk Perceptions Among Black Men in Florida.

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.

Nightmare disorder and low back pain in veterans: cross-sectional association and effect over time.

Low back pain (LBP) disproportionately impacts US military veterans compared with nonveterans. Although the effect of psychological conditions on LBP is regularly studied, there is little published to date investigating nightmare disorder (NMD) and LBP. The purpose of this study was to (1) investigate whether an association exists between NMD and LBP and (2) estimate the effect of NMD diagnosis on time to LBP. We used a retrospective cohort design with oversampling of those with NMD from the Veterans Health Administration (n = 15 983). We used logistic regression to assess for a cross-sectional association between NMD and LBP and survival analysis to estimate the effect of NMD on time to LBP, up to 60-month follow-up, conditioning on age, sex, race, index year, Charlson Comorbidity Index, depression, anxiety, insomnia, combat exposure, and prisoner of war history to address confounding. Odds ratios (with 95% confidence intervals [CIs]) indicated a cross-sectional association of 1.35 (1.13 to 1.60) and 1.21 (1.02 to 1.42) for NMD and LBP within 6 months and 12 months pre- or post-NMD diagnosis, respectively. Hazard ratios (HRs) indicated the effect of NMD on time to LBP that was time-dependent-HR (with 95% CIs) 1.27 (1.02 to 1.59), 1.23 (1.03 to 1.48), 1.19 (1.01 to 1.40), and 1.10 (0.94 to 1.29) in the first 3, 6, 9, and 12 months post-diagnosis, respectively-approximating the null (1.00) at >12 months. The estimated effect of NMD on LBP suggests that improved screening for NMD among veterans may help clinicians and researchers predict (or intervene to reduce) risk of future back pain.

Geographic variation and association of risk factors with incidence of colorectal cancer at small-area level.

PURPOSE: Examining 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. METHODS: County-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. RESULTS: The 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. CONCLUSION: Among 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.

Bayesian censored piecewise regression mixture models with skewness.

This paper presents censored mixture regression models with piecewise growth curves for assessing longitudinal data that exhibit multiphasic features. Such features may include censoring, skewness, measurement errors in covariates, and mixtures of unobserved subpopulations. In the process of describing those features, identification of differential effects of predictors on a response variable for a heterogeneous population (subpopulations) has recently been highly sought. Regression mixture models are key methods for assessing differential effects of predictors. In this article, we extend regression mixture models with normal distribution to incorporate (i) skew-normal distribution, (ii) left-censoring, (iii) measurement errors, and (iv) piecewise growth mixture modeling for describing multiphasic trajectories over time where the observed observations come from a mixture of unobserved subgroups. The proposed methods are illustrated using real data from an AIDS clinical study and a Bayesian approach.

Development of a Minority Prostate Cancer Research Digest: Communication Strategy Statement for Black Men.

Since prostate cancer incidence, prevalence and mortality are still highest among Black men in the United States, it is important to effectively address the factors that contribute to prostate cancer disparities in this at-risk population as well as their low participation in biomedical research/clinical trials. An effective communication strategy that can be used to disseminate information with high public health impact to Black men is one way to combat prostate cancer disparities. The objective of this study was to develop a Minority Prostate Cancer (MiCaP) research communication strategy using focus group methodology and expert in-depth interviews. The communication strategy statement developed in this study provides a guide for message concepts and materials for Black men, including communication content, source, channel, and location. Specifically, it provides recommendations on how to deliver information, how to choose the language and relevant images, how to gain attention, who is preferred to deliver messages, and other ways to engage Black men in health communication strategies. The communication strategy statement was used to develop the MiCaP Research Digest, a research communication program that is currently being tested in Orange County, Duval County, Leon County, Gadsden County, and the Tampa Bay area of Florida.

Multilevel modeling, prevalence, and predictors of hypertension in Ghana: Evidence from Wave 2 of the World Health Organization's Study on global AGEing and adult health.

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.

Differential privacy in health research: A scoping review.

OBJECTIVE: Differential privacy is a relatively new method for data privacy that has seen growing use due its strong protections that rely on added noise. This study assesses the extent of its awareness, development, and usage in health research. MATERIALS AND METHODS: A scoping review was conducted by searching for ["differential privacy" AND "health"] in major health science databases, with additional articles obtained via expert consultation. Relevant articles were classified according to subject area and focus. RESULTS: A total of 54 articles met the inclusion criteria. Nine articles provided descriptive overviews, 31 focused on algorithm development, 9 presented novel data sharing systems, and 8 discussed appraisals of the privacy-utility tradeoff. The most common areas of health research where differential privacy has been discussed are genomics, neuroimaging studies, and health surveillance with personal devices. Algorithms were most commonly developed for the purposes of data release and predictive modeling. Studies on privacy-utility appraisals have considered economic cost-benefit analysis, low-utility situations, personal attitudes toward sharing health data, and mathematical interpretations of privacy risk. DISCUSSION: Differential privacy remains at an early stage of development for applications in health research, and accounts of real-world implementations are scant. There are few algorithms for explanatory modeling and statistical inference, particularly with correlated data. Furthermore, diminished accuracy in small datasets is problematic. Some encouraging work has been done on decision making with regard to epsilon. The dissemination of future case studies can inform successful appraisals of privacy and utility. CONCLUSIONS: More development, case studies, and evaluations are needed before differential privacy can see widespread use in health research.

Geostatistical analysis, web-based mapping, and environmental determinants of under-5 stunting: evidence from the 2014 Ghana Demographic and Health Survey.

BACKGROUND: Stunting rates in children younger than 5 years are among the most important health indicators globally. At the national level, malnutrition accounts for about 40% of under-5 deaths in Ghana. Disease risk mapping provides opportunities for disease surveillance and targeted interventions. We aimed to estimate and map under-5 stunting prevalence in Ghana, with the goal of identifying communities at higher risk where interventions and further research can be targeted. METHODS: For this modelling study, we used data from the 2014 Ghana Demographic and Health Survey. Analyses were done on 2734 children residing in 415 geographical clusters. The outcome variable was the number of stunted children younger than 5 years in each sampled cluster. We employed a Bayesian geostatistical model to investigate both measured and unmeasured spatial risk factors for child stunting, comparing the performance of non-spatial (adjusting for selected covariates without spatial correlation), spatial (including spatial correlation), and null spatial (without the selected covariates) models. We then visualised the stunting prevalence across Ghana by mapping the predicted prevalence and exceedance probabilities to resolutions as refined as 5 km × 5 km. FINDINGS: In 2014, 535 (19·6%) of 2734 children surveyed in Ghana were stunted. Elevation (log odds mean -0·0017, 95% credible interval -0·0034 to -0·0001), precipitation (0·0403, 0·0192 to 0·0615), and aridity (-3·7013, -6·5478 to -0·8723) were environmental and climatic factors associated with stunting in the non-spatial model, but were not significant in the spatial model. Substantial geographical variations in prevalence of childhood stunting were found. The predicted mean stunting prevalence was 27·7% (SD 3·7%) with predicted prevalence ranging from 4·2% to 45·1% across Ghana. Children residing in parts of the Northern region were at highest risk of stunting, whereas parts of the Greater Accra, Brong-Ahafo, Ashanti, and Eastern regions showed some of the lowest prevalence. INTERPRETATION: There are substantial geographical differences in childhood stunting across Ghana. Our prevalence maps can be used as an effective tool to identify communities that require targeted interventions by programme managers and implementers, as part of an overall strategy to reduce the burden of malnutrition in a country with limited public health resources. FUNDING: None.

Geospatial analysis, web-based mapping and determinants of prostate cancer incidence in Georgia counties: evidence from the 2012-2016 SEER data.

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.

Neoadjuvant Chemotherapy for Intrahepatic Cholangiocarcinoma: A Propensity Score Survival Analysis Supporting Use in Patients with High-Risk Disease.

BACKGROUND: Upfront 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. METHODS: The 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. RESULTS: Among 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). CONCLUSION: NAC 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.

Prevalence of Depression and Associated Factors among Diabetes Patients in East Shewa, Ethiopia: Bayesian Approach.

BACKGROUND: Depression is one of the most pressing public health problems and also highly prevalent comorbid condition among diabetes mellitus (DM) patients. Depression may impact lifestyle decisions and ability to poorly perform tasks which are risk factors for DM. For reducing the impact of depression among DM patients in developing countries, it is crucial to identify and assess associated risk factors of depression among DM patients, thereby designing effective management techniques. In line with this, the current study applies the Bayesian framework, which pools prior information and current data, to find factors associated with depression among DM patients. METHODS: A hospital-based cross-sectional study was conducted at Adama Hospital and Medical College (AHMC) from March to April 2019. Data was entered into the Epi-data 3.1 then exported to the R software 3.4.4. Bayesian logistic regression models were fitted to the data using the Markov chain Monte Carlo (MCMC) algorithm. Estimates of model parameters including adjusted odds ratio (AOR) with 95% credible intervals (CI) were calculated. RESULTS: A total of 359 adults with DM were included in the analysis. The prevalence of depression among diabetic patients was 9.22% (95% CI: 6.4% to 12.7%). Higher fasting blood sugar level (AOR = -1.012; HPD CI: (1.0020, 1.025)), having diabetic complication (AOR = 0.1876; HPD CI: (0.0214, 0.671)), history of hospital admission (AOR = 0.2865; HPD CI: (0.0711, 0.7318)), low medication adherence (AOR = 29.29; HPD CI: (3.383, 92.26)), and taking both insulin and oral antidiabetic medication (AOR = 24.46; HPD CI: (15.20, 49.37) were significantly and strongly associated with depression among DM patients. CONCLUSIONS: Prevalence of depression among diabetes patients in the catchment area of Adama Hospital, Ethiopia, was found to be very low. Higher fasting blood sugar level, diabetic complication, history of hospital admission, low medication adherence, and taking both insulin and oral antidiabetic medication were found to be strong predictors of prevalence of depression among DM patients. Based on the findings, we recommend that integrating screening and treating of depression, early detection and management of diabetic complication, and giving counseling to improve medication adherence is an effective approach for lowering the impact of depression on DM patients.

Determinants of malaria among under-five children in Ethiopia: Bayesian multilevel analysis.

BACKGROUND: In Ethiopia, malaria is one of the public health problems, and it is still among the ten top leading causes of morbidity and mortality among under-five children. However, the studies conducted in the country have been inconclusive and inconsistent. Thus, this study aimed to assess factors associated with malaria among under-five children in Ethiopia. METHODS: We retrieved secondary data from the malaria indicator survey data collected from September 30 to December 10, 2015, in Ethiopia. A total of 8301 under-five-year-old children who had microscopy test results were included in the study. Bayesian multilevel logistic regression models were fitted and Markov chain Monte Carlo simulation was used to estimate the model parameters using Gibbs sampling. Adjusted Odd Ratio with 95% credible interval in the multivariable model was used to select variables that have a significant association with malaria. RESULTS: In this study, sleeping under the insecticide-treated bed nets during bed time (ITN) [AOR 0.58,95% CI, 0.31-0.97)], having 2 and more ITN for the household [AOR 0.43, (95% CI, 0.17-0.88)], have radio [AOR 0.41, (95% CI, 0.19-0.78)], have television [AOR 0.19, (95% CI, 0.01-0.89)] and altitude [AOR 0.05, (95% CI, 0.01-0.13)] were the predictors of malaria among under-five children. CONCLUSIONS: The study revealed that sleeping under ITN, having two and more ITN for the household, altitude, availability of radio, and television were the predictors of malaria among under-five children in Ethiopia. Thus, the government should strengthen the availability and utilization of ITN to halt under-five mortality due to malaria.

Bayesian semiparametric growth models for measurement error and missing data in CD4/CD8 ratio: Application to AIDS Study.

In clinical research and practice, there is often an interest in assessing the effect of time varying predictors, such as CD4/CD8 ratio, on immune recovery following antiretroviral therapy. Such predictors are measured with errors, and ignoring those measurement errors during data analysis may lead to biased results. Though parametric methods have been used for reducing biases, they usually depend on untestable assumptions. To relax those assumptions, this paper presents semiparametric mixed-effect models which deal with predictors having measurement errors and missing values. We develop a fully Bayesian approach for fitting these models and discriminating between patients who are potentially progressors or nonprogressors to severe disease condition (AIDS). The proposed methods are demonstrated using real data from an AIDS clinical study.

Evaluating construct equivalence of youth depression measures across multiple measures and multiple studies.

Construct equivalence of measures across studies is necessary for synthesizing results when combining data in meta-analysis or integrative data analysis. We discuss several assumptions required for construct equivalence, and review methods using individual-level data and item response theory (IRT) analysis for detecting or adjusting for violations of these assumptions. We apply IRT to data from 7 measures of depressive symptoms for 4,283 youth from 16 randomized prevention trials. Findings indicate that these data violate assumptions of conditional independence. Bifactor IRT models find that depression measures contain substantial reporter variance, and indicate that a single common factor model would be substantially biased. Separate analyses of ratings by youth find stronger evidence for construct equivalence, but factor invariance across sex and age does not hold. We conclude that data synthesis studies employing measures of youth depression should analyze results separately by reporter, explore more complex approaches to integrate these different perspectives, and explore methods that adjust for sex and age differences in item functioning. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Food and Nutrient Intakes of Jamaican Immigrants in Florida.

This study assessed dietary intakes, nutritional composition, and identified commonly eaten foods among Jamaicans in Florida. Dietary intake was assessed among 44 study participants to determine commonly eaten foods and nutrient composition. Weighed recipes were collected and analyzed to determine nutrient composition for traditional foods. Top foods that contributed to macronutrient and micronutrient intake were identified and adherence to dietary recommendations was evaluated. Mean daily energy intake was 2879 (SD 1179) kcal and 2242 (SD 1236) kcal for men and women respectively. Mean macronutrient intakes were above dietary recommendations for men and women. Top foods contributing to energy included rice and peas, sweetened juices, chicken, red peas soup, and hot chocolate drink. Results showed sodium intake was more than double the adequate intake estimate (1300-1500 mg). Findings highlight the need to include commonly eaten traditional foods in dietary questionnaires to accurately assess diet-related chronic disease risk. Findings have implications for risk factor intervention and prevention efforts among Jamaicans.