Understanding social risk factors of county-level disparities in COVID-19 tests per confirmed case in South Carolina using statewide electronic health records data.

BACKGROUND: COVID-19 testing is essential for pandemic control, and insufficient testing in areas with high disease burdens could magnify the risk of poor health outcomes. However, few area-based studies on COVID-19 testing disparities have considered the disease burden (e.g., confirmed cases). The current study aims to investigate socioeconomic drivers of geospatial disparities in COVID-19 testing relative to disease burden across 46 counties in South Carolina (SC) in the early (from April 1, 2020, to June 30, 2020) and later (from July 1, 2020, to September 30, 2021) phases of the pandemic. METHODS: Using SC statewide COVID-19 testing data, the COVID-19 testing coverage was measured by monthly COVID-19 tests per confirmed case (hereafter CTPC) in each county. We used modified Lorenz curves to describe the unequal geographic distribution of CTPC and generalized linear mixed-effects regression models to assess the association of county-level social risk factors with CTPC in two phases of the pandemic in SC. RESULTS: As of September 30, 2021, a total of 641,201 out of 2,941,227 tests were positive in SC. The Lorenz curve showed that county-level disparities in CTPC were less apparent in the later phase of the pandemic. Counties with a larger percentage of Black had lower CTPC during the early phase (ß = -0.94, 95%CI: -1.80, -0.08), while such associations reversed in the later phase (ß = 0.28, 95%CI: 0.01, 0.55). The association of some other social risk factors diminished as the pandemic evolved, such as food insecurity (ß: -1.19 and -0.42; p-value is < 0.05 for both). CONCLUSIONS: County-level disparities in CTPC and their predictors are dynamic across the pandemic. These results highlight the systematic inequalities in COVID-19 testing resources and accessibility, especially in the early stage of the pandemic. Counties with greater social vulnerability and those with fewer health care resources should be paid extra attention in the early and later phases, respectively. The current study provided empirical evidence for public health agencies to conduct more targeted community-based testing campaigns to enhance access to testing in future public health crises.

How Different Pre-existing Mental Disorders and Their Co-occurrence Affects COVID-19 Clinical Outcomes? A Real-World Data Study in the Southern United States.

Background: Although a psychiatric history might be an independent risk factor for COVID-19 infection and mortality, no studies have systematically investigated how different clusters of pre-existing mental disorders may affect COVID-19 clinical outcomes or showed how the coexistence of mental disorder clusters is related to COVID-19 clinical outcomes. Methods: Using a retrospective cohort study design, a total of 476,775 adult patients with lab-confirmed and probable COVID-19 between March 06, 2020 and April 14, 2021 in South Carolina, United States were included in the current study. The electronic health record data of COVID-19 patients were linked to all payer-based claims data through the SC Revenue and Fiscal Affairs Office. Pre-existing mental disorder diagnoses from Jan 2, 2019 to Jan 14, 2021 were extracted from the patients' healthcare utilization data via ICD-10 codes. Results: There is an elevated risk of COVID-19-related hospitalization and death among participants with pre-existing mental disorders adjusting for key socio-demographic and comorbidity covariates. Co-occurrence of any two clusters was positively associated with COVID-19-related hospitalization and death. The odds ratio of being hospitalized was 1.26 (95% CI: 1.151, 1.383) for patients with internalizing and externalizing disorders, 1.65 (95% CI: 1.298, 2.092) for internalizing and thought disorders, 1.76 (95% CI: 1.217, 2.542) for externalizing and thought disorders, and 1.64 (95% CI: 1.274, 2.118) for three clusters of mental disorders. Conclusions: Pre-existing internalizing disorders and thought disorders are positively related to COVID-19 hospitalization and death. Co-occurrence of any two clusters of mental disorders have elevated risk of COVID-19-related hospitalization and death compared to those with a single cluster.

Population Mobility and Aging Accelerate the Transmission of Coronavirus Disease 2019 in the Deep South: A County-Level Longitudinal Analysis.

Population mobility and aging at local areas contributed to the geospatial disparities in the coronavirus disease 2019 (COVID-19) transmission among 418 counties in the Deep South. In predicting the incidence of COVID-19, a significant interaction was found between mobility and the proportion of older adults. Effective disease control measures should be tailored to vulnerable communities.

Social Capital, Urbanization Level, and COVID-19 Vaccination Uptake in the United States: A National Level Analysis.

Vaccination remains the most promising mitigation strategy for the COVID-19 pandemic. However, existing literature shows significant disparities in vaccination uptake in the United States. Using publicly available national-level data, we aimed to explore if county-level social capital can further explain disparities in vaccination uptake rates when adjusting for demographic and social determinants of health (SDOH) variables, and if association between social capital and vaccination uptake may vary by urbanization level. Bivariate analyses and a hierarchical multivariable quasi-binomial regression analysis were conducted, where the regression analysis was stratified by urban-rural status. The current study suggests that social capital contributes significantly to the disparities of vaccination uptake in the US. The results of the stratification analysis show common predictors of vaccine uptake but also suggest various patterns based on urbanization level regarding the associations of COVID-19 vaccination uptake with SDOH and social capital factors. The study provides a new perspective to address disparities in vaccination uptake through fostering social capital within communities; which may inform tailored public health intervention efforts to enhance social capital and promote vaccination uptake.

Examining failure learning in online lending: Complete failure vs. incomplete failure.

We examine the learning effects of borrowers' failures in online lending. Based on funding ratios of borrowers' loan listings in online lending, we first explore the role of failure degree in borrowers' future funding performance. Further, we disaggregate borrowers' funding failure into complete failure and incomplete failure, and compare theirs learning effects. Using a large sample of 610,000 online loan applications over six years from a Chinese leading online lending platform Renrendai, we use funding ratio to quantifiably measure each loan listing's failure degree and conduct a series of tests. The results show that: (1) Borrowers' failure degree of prior loan applications is negatively associated with one's subsequent funding performance. (2) Borrowers' complete failure cannot promote learning, while incomplete failure is good for future performance. (3) Both incomplete failure and complete failure interacted to influence the value of each type of experience and generate improved learning. Our results are robust across a variety of settings. The study sheds light for deeply understanding of failure learning phenomenon, and can also provide important implications for online lending managers to support successful financial transactions.

Demographic Disparities in Clinical Outcomes of COVID-19: Data From a Statewide Cohort in South Carolina.

BACKGROUND: Current literature examining the clinical characteristics of coronavirus disease 2019 (COVID-19) patients under-represent COVID-19 cases who were either asymptomatic or had mild symptoms. METHODS: We analyzed statewide data from 280 177 COVID-19 cases from various health care facilities during March 4-December 31, 2020. Each COVID-19 case was reported using the standardized Case Report Form (CRF), which collected information on demographic characteristics, symptoms, hospitalization, and death. We used multivariable logistic regression to analyze the associations between sociodemographics and disease severity, hospitalization, and mortality. RESULTS: Among a total of 280 177 COVID-19 cases, 5.2% (14 451) were hospitalized and 1.9% (5308) died. Older adults, males, and Black individuals had higher odds of hospitalization and death from COVID-19 (all P < 0.0001). In particular, individuals residing in rural areas experienced a high risk of death (odds ratio [OR], 1.16; 95% CI, 1.08-1.25). Regarding disease severity, older adults (OR, 1.06; 95% CI, 1.03-1.10) and Hispanic or Latino patients (OR, 2.06; 95% CI, 1.95-2.18) had higher odds of experiencing moderate/severe symptoms, while male and Asian patients, compared with White patients, had lower odds of experiencing moderate/severe symptoms. CONCLUSIONS: As the first statewide population-based study using data from multiple health care systems with a long follow-up period in the United States, we provide a more generalizable picture of COVID-19 symptoms and clinical outcomes. The findings from this study reinforce the fact that rural residence and racial/ethnic social determinants of health, unfortunately, remain predictors of adverse health outcomes for COVID-19 patients.

Social Determinants of Racial Disparities in Breast Cancer Mortality Among Black and White Women.

INTRODUCTION: Mortality from breast cancer among Black women is 60% greater than that of White women in South Carolina (SC). The aim of this study was to assess racial differences in mortality among Black and White breast cancer patients based on variations in social determinants and access to state-based early detection programs. METHODS: We obtained a retrospective record for breast cancer patients diagnosed between 2002 and 2010 from the SC Central Cancer Registry. Mortality was the main outcome while race-stratified Cox proportional hazard models were performed to assess disparities in mortality. We assessed effect modification, and we used an automated backward elimination process to obtain the best fitting models. RESULTS: There were 3286 patients of which the majority were White women (2186, 66.52%). Compared with married White women, the adjusted hazard ratio (aHR) for mortality was greatest among Black unmarried women (aHR 2.31, CI 1.83, 2.91). Compared with White women who lived in the Low Country region mortality was greatest among Black women who lived in the Midland (aHR 2.17 CI 1.47, 3.21) and Upstate (aHR 2.96 CI 1.96, 2.49). Mortality was higher among Black women that were not receiving services in the Best Chance Network (BCN) program (aHR 1.70, CI 1.40, 2.04) compared with White women. CONCLUSIONS: To reduce the racial disparity gap in survival in SC, Black breast cancer patients who live in the Upstate, are unmarried, and those that are not enrolled in the BCN program may benefit from more intense navigation efforts directed at early detection and linkage to breast cancer treatments.

Rural-urban and racial/ethnic trends and disparities in early-onset and average-onset colorectal cancer.

BACKGROUND: Incidence rates (IRs) of early-onset colorectal cancer (EOCRC) are increasing, whereas average-onset colorectal cancer (AOCRC) rates are decreasing. However, rural-urban and racial/ethnic differences in trends by age have not been explored. The objective of this study was to examine joint rural-urban and racial/ethnic trends and disparities in EOCRC and AOCRC IRs. METHODS: Surveillance, Epidemiology, and End Results data on the incidence of EOCRC (age, 20-49 years) and AOCRC (age, ≥50 years) were analyzed. Annual percent changes (APCs) in trends between 2000 and 2016 were calculated jointly by rurality and race/ethnicity. IRs and rate ratios were calculated for 2012-2016 by rurality, race/ethnicity, sex, and subsite. RESULTS: EOCRC IRs increased 35% from 10.44 to 14.09 per 100,000 in rural populations (APC, 2.09; P < .05) and nearly 20% from 9.37 to 11.20 per 100,000 in urban populations (APC, 1.26; P < .05). AOCRC rates decreased among both rural and urban populations, but the magnitude of improvement was greater in urban populations. EOCRC increased among non-Hispanic White (NHW) populations, although rural non-Hispanic Black (NHB) trends were stable. Between 2012 and 2016, EOCRC IRs were higher among all rural populations in comparison with urban populations, including NHW, NHB, and American Indian/Alaska Native populations. By sex, rural NHB women had the highest EOCRC IRs across subgroup comparisons, and this was driven primarily by colon cancer IRs 62% higher than those of their urban peers. CONCLUSIONS: EOCRC IRs increased in rural and urban populations, but the increase was greater in rural populations. NHB and American Indian/Alaska Native populations had particularly notable rural-urban disparities. Future research should examine the etiology of these trends.

A high-density SNP-based genetic map and several economic traits-related loci in Pelteobagrus vachelli.

BACKGROUND: A high-density genetic linkage map is essential for QTL fine mapping, comparative genome analysis, identification of candidate genes and marker-assisted selection in aquaculture species. Pelteobagrus vachelli is a very popular commercial species in Asia. However, some specific characters hindered achievement of the traditional selective breeding based on phenotypes, such as lack of large-scale genomic resource and short of markers tightly associated with growth, sex determination and hypoxia tolerance related traits. RESULTS: By making use of 5059 ddRAD markers in P. vachelli, a high-resolution genetic linkage map was successfully constructed. The map' length was 4047.01 cM by using an interval of 0.11 cm, which is an average marker standard. Comparative genome mapping revealed that a high proportion (83.2%) of markers with a one-to-one correspondence were observed between P. vachelli and P. fulvidraco. Based on the genetic map, 8 significant genome-wide QTLs for 4 weight, 1 body proportion, 2 sex determination, and 1 hypoxia tolerance related traits were detected on 4 LGs. Some SNPs from these significant genome-wide QTLs were observably associated with these phenotypic traits in other individuals by Kompetitive Allele Specific PCR. In addition, two candidate genes for weight, Sipa1 and HSD11B2, were differentially expressed between fast-, medium- and slow-growing P. vachelli. Sema7a, associated with hypoxia tolerance, was induced after hypoxia exposure and reoxygenation. CONCLUSIONS: We mapped a set of suggestive and significant QTLs as well as candidate genes for 12 growth, 1 sex determination and 1 hypoxia tolerance related traits based on a high-density genetic linkage map by making use of SNP markers for P. fulvidraco. Our results have offered a valuable method about the much more efficient production of all-male, fast growth and hypoxia tolerance P. vachelli for the aquaculture industry.

Left ventricular mechanical dyssynchrony assessment in obese patients using the cadmium-zinc telluride SPECT camera.

The use of phase analysis techniques to assess left ventricular mechanical dyssynchrony (LVMD) has been well documented. However, artifacts have reduced the accuracy of the assessment due to soft tissue attenuation, so little information is available about the effects of obesity on LVMD. The aim of this study was to evaluate LVMD in patients with simple obesity by SPECT with a new cadmium-zinc telluride (CZT) detector and to explore the effects of obesity on left ventricular wall motion. We retrospectively analyzed 95 patients with myocardial perfusion imaging (MPI) images without perfusion defects, of which 55 were diagnosed with simple obesity (BMI > 30), and 40 non-obese patients (BMI < 25) matched for age and sex were used as controls. The five-point method was used to analyze the MPI images of the two groups, and the complete cardiac function parameters including phase bandwidth (PBW) and phase standard deviation (PSD) were obtained. Although the PBW values of the two groups were within the normal range (cut-off value > 90°), the PBW (35.4 ± 28 vs 24.9 ± 7.5, P < .001; 36.6 ± 18.4 vs 28.7 ± 9.1, P = 0.01) and PSD (8.7 ± 7.6 vs 5.9 ± 2, P = 0.02; 9.2 ± 4.9 vs 7.1 ± 2.7, P = 0.01) of the obese group were larger than the control group under both stressing and resting, and the difference was statistically significant. CZT-SPECT can effectively assess LVMD in obese patients, and they are more likely to develop LVMD, which may be related to their left ventricular volume.

Prenatal diagnosis of genetic diseases directly using paper-dried cord blood as the starting material for PCR.

A rapid and low-cost method of diagnosis is becoming important for detecting fetal inherited diseases, including single-gene disorders and chromosomal abnormalities. Here, we demonstrated an innovation that use paper-dried cord blood (PCB) as the starting material for PCR and whole genome amplification without any DNA extraction step at a very low cost. A novel PCR buffer named "DDB buffer" containing ammonium sulfate and glycerol were used instead of the conventional 10× PCR buffer. The amplicons were directly analyzed through microchip electrophoresis and whole genome sequencing. Inhibitory substances in filter paper were effectively inactivated using DDB buffer. Direct PCR amplification of DNA fragments ranging from 100 to 900 bp using filter paper spotted with 0.5 to 5 µL of cord blood and various anticoagulants was successful. We were able to determine fetal single-gene disorders and chromosomal diseases in all 46 chromosomes using PCB samples successfully. Compared with prenatal diagnosis using purified DNA, the proposed method is simple, fast, less prone to cross-contamination at minimal cost. Researchers and clinical and healthcare workers may employ this method for genetic diagnosis using cord blood samples with minimum laboratory resources. This method is very promising for a variety of genetic diagnosis applications in underserved communities at the point of need in developing areas. Graphical abstract.

Using big data analytics to improve HIV medical care utilisation in South Carolina: A study protocol.

INTRODUCTION: Linkage and retention in HIV medical care remains problematic in the USA. Extensive health utilisation data collection through electronic health records (EHR) and claims data represent new opportunities for scientific discovery. Big data science (BDS) is a powerful tool for investigating HIV care utilisation patterns. The South Carolina (SC) office of Revenue and Fiscal Affairs (RFA) data warehouse captures individual-level longitudinal health utilisation data for persons living with HIV (PLWH). The data warehouse includes EHR, claims and data from private institutions, housing, prisons, mental health, Medicare, Medicaid, State Health Plan and the department of health and human services. The purpose of this study is to describe the process for creating a comprehensive database of all SC PLWH, and plans for using BDS to explore, identify, characterise and explain new predictors of missed opportunities for HIV medical care utilisation. METHODS AND ANALYSIS: This project will create person-level profiles guided by the Gelberg-Andersen Behavioral Model and describe new patterns of HIV care utilisation. The population for the comprehensive database comes from statewide HIV surveillance data (2005-2016) for all SC PLWH (N≈18000). Surveillance data are available from the state health department's enhanced HIV/AIDS Reporting System (e-HARS). Additional data pulls for the e-HARS population will include Ryan White HIV/AIDS Program Service Reports, Health Sciences SC data and Area Health Resource Files. These data will be linked to the RFA data and serve as sources for traditional and vulnerable domain Gelberg-Anderson Behavioral Model variables. The project will use BDS techniques such as machine learning to identify new predictors of HIV care utilisation behaviour among PLWH, and 'missed opportunities' for re-engaging them back into care. ETHICS AND DISSEMINATION: The study team applied for data from different sources and submitted individual Institutional Review Board (IRB) applications to the University of South Carolina (USC) IRB and other local authorities/agencies/state departments. This study was approved by the USC IRB (#Pro00068124) in 2017. To protect the identity of the persons living with HIV (PLWH), researchers will only receive linked deidentified data from the RFA. Study findings will be disseminated at local community forums, community advisory group meetings, meetings with our state agencies, local partners and other key stakeholders (including PLWH, policy-makers and healthcare providers), presentations at academic conferences and through publication in peer-reviewed articles. Data security and patient confidentiality are the bedrock of this study. Extensive data agreements ensuring data security and patient confidentiality for the deidentified linked data have been established and are stringently adhered to. The RFA is authorised to collect and merge data from these different sources and to ensure the privacy of all PLWH. The legislatively mandated SC data oversight council reviewed the proposed process stringently before approving it. Researchers will get only the encrypted deidentified dataset to prevent any breach of privacy in the data transfer, management and analysis processes. In addition, established secure data governance rules, data encryption and encrypted predictive techniques will be deployed. In addition to the data anonymisation as a part of privacy-preserving analytics, encryption schemes that protect running prediction algorithms on encrypted data will also be deployed. Best practices and lessons learnt about the complex processes involved in negotiating and navigating multiple data sharing agreements between different entities are being documented for dissemination.

High-quality and low-cost three-dimensional graphene from graphite flakes via carbocation-induced interlayer oxygen release.

Three-dimensional (3D) graphene can fully bring the excellent properties of graphene into wide utilization. However, its production requires the use of graphene oxide as the intermediate or harsh chemical vapour deposition conditions, which do not meet the requirements of high quality and low cost simultaneously. Herein, we report a room-temperature low-cost strategy to produce 3D graphene under ambient conditions, which is achieved via carbocation-induced interlayer oxygen release. Graphite layers can transfer electrons to hydrogen peroxide, forming carbocations, which in turn oxidize hydrogen peroxide to release O2, substantially enlarging the interlayer space of graphite. We show that graphite can expand 1000 times or more into the liquid phase and no oxygen-containing groups are introduced on the basal plane. The obtained 3D graphene has an open porous structure and a specific surface area (SSA) of 1245 m2 g-1, which is comparable to that of CVD-grown 3D graphene networks and equivalent to that of 2-layer graphene. Moreover, the quality of 3D graphene is high, with ID/IG smaller than 0.2. The as-obtained 3D graphene can be easily exfoliated into graphene sheets with 100% yield. Gifted with well-maintained crystalline quality and highly accessible surface area, the 3D graphene-based composite exhibits ultra-long life when used as lithium anode, with slight capacity degradation after 1000 cycles.

Pathways linking socioeconomic status to small-for-gestational-age (SGA) infants among primiparae: a birth cohort study in China.

OBJECTIVES: Evidence about the relationship between socioeconomic status (SES) and small-for-gestational-age (SGA) infants was insufficient among Chinese primiparae. In addition, factors that may mediate this relationship are poorly understood. The purpose of this study was to investigate the risk of and mediators between SES and SGA. DESIGN: Retrospective cohort study. SETTING: Wuhan, Hubei, China. METHOD: Participants were recruited from patients who gave birth in the maternity care hospital of Wuhan between September 2012 and October 2014. Logistic regression models were used to estimate the association between SES and SGA. Pathway analysis was performed to examine the contribution of maternal lifestyles and pregnancy-induced hypertension syndrome (PIH) to the relationship between SES and SGA. Total effect, direct effect and indirect effect of SES on SGA were measured. Effect sizes were evaluated by unstandardised estimates (B) and standardised estimates (ß). RESULTS: Among 8737 primiparae, 927 (10.61%) pregnant women had babies with SGA. High SES was inversely associated with risk of SGA (OR 0.856; 95% CI 0.737 to 0.995) after adjustment for potential confounders. Maternal obstetric characteristics, lifestyles and PIH completely mediated SES and SGA (indirect effect: B=-0.067, 95% CI -0.108 to -0.026). The indirect effect of SES was strengthened by PIH (B=-0.029), a multivitamin supplement (B=-0.021), prepregnancy body mass index (BMI) ≥18.50 (B=-0.009) and prepregnancy BMI ≥18.50 to gestational weight gain (GWG) not below the Institute of Medicine (IOM) recommendations (B=-0.003). CONCLUSIONS: Women from high SES predicted lower risk of PIH, more chances to take a multivitamin supplement during early pregnancy, keeping prepregnancy BMI ≥18.50 kg/cm2 and gaining adequate gestational weight which was not below IOM recommendations. Furthermore, lower risk of PIH, more chances to take a multivitamin supplement, prepregnancy BMI ≥18.50 kg/cm2 and GWG not below IOM recommendations were associated with a lower risk of SGA infants.

Rapid and cost-effective method for the simultaneous quantification of seven alkaloids in Corydalis decumbens by microwave-assisted extraction and capillary electrophoresis.

A rapid and cost-effective method based on microwave-assisted extraction followed by capillary electrophoresis was developed for simultaneous quantification of seven alkaloids in Corydalis decumbens for the first time. The main parameters affecting microwave-assisted extraction and capillary electrophoresis separation were investigated and optimized. The optimal microwave-assisted extraction was performed at 40°C for 5 min using methanol/water (90:10, v/v) as the extracting solvent. Electrophoretic separation was achieved within 15 min using an uncoated fused-silica capillary (50 µm internal diameter and 27.7 cm effective length) and a 500 mM Tris buffer containing 45% v/v methanol (titrated to pH* 2.86 with H3 PO4 ). The developed method was successfully applied to the quantification of seven alkaloids in Corydalis decumbens obtained from different regions of China. The combination of microwave-assisted extraction with capillary electrophoresis was an effective method for the rapid analysis of the alkaloids in Corydalis decumbens.

Zoeppritz-based AVO inversion using an improved Markov chain Monte Carlo method.

The conventional Markov chain Monte Carlo (MCMC) method is limited to the selected shape and size of proposal distribution and is not easy to start when the initial proposal distribution is far away from the target distribution. To overcome these drawbacks of the conventional MCMC method, two useful improvements in MCMC method, adaptive Metropolis (AM) algorithm and delayed rejection (DR) algorithm, are attempted to be combined. The AM algorithm aims at adapting the proposal distribution by using the generated estimators, and the DR algorithm aims at enhancing the efficiency of the improved MCMC method. Based on the improved MCMC method, a Bayesian amplitude versus offset (AVO) inversion method on the basis of the exact Zoeppritz equation has been developed, with which the P- and S-wave velocities and the density can be obtained directly, and the uncertainty of AVO inversion results has been estimated as well. The study based on the logging data and the seismic data demonstrates the feasibility and robustness of the method and shows that all three parameters are well retrieved. So the exact Zoeppritz-based nonlinear inversion method by using the improved MCMC is not only suitable for reservoirs with strong-contrast interfaces and long-offset ranges but also it is more stable, accurate and anti-noise.

Bayesian accelerated failure time model for space-time dependency in a geographically augmented survival model.

In this paper, we extend the spatially explicit survival model for small area cancer data by allowing dependency between space and time and using accelerated failure time models. Spatial dependency is modeled directly in the definition of the survival, density, and hazard functions. The models are developed in the context of county level aggregated data. Two cases are considered: the first assumes that the spatial and temporal distributions are independent; the second allows for dependency between the spatial and temporal components. We apply the models to prostate cancer data from the Louisiana SEER cancer registry.

Generalized accelerated failure time spatial frailty model for arbitrarily censored data.

Flexible incorporation of both geographical patterning and risk effects in cancer survival models is becoming increasingly important, due in part to the recent availability of large cancer registries. Most spatial survival models stochastically order survival curves from different subpopulations. However, it is common for survival curves from two subpopulations to cross in epidemiological cancer studies and thus interpretable standard survival models can not be used without some modification. Common fixes are the inclusion of time-varying regression effects in the proportional hazards model or fully nonparametric modeling, either of which destroys any easy interpretability from the fitted model. To address this issue, we develop a generalized accelerated failure time model which allows stratification on continuous or categorical covariates, as well as providing per-variable tests for whether stratification is necessary via novel approximate Bayes factors. The model is interpretable in terms of how median survival changes and is able to capture crossing survival curves in the presence of spatial correlation. A detailed Markov chain Monte Carlo algorithm is presented for posterior inference and a freely available function frailtyGAFT is provided to fit the model in the R package spBayesSurv. We apply our approach to a subset of the prostate cancer data gathered for Louisiana by the surveillance, epidemiology, and end results program of the National Cancer Institute.

Types of oral contraceptives and breast cancer survival among women enrolled in Medicaid: A competing-risk model.

INTRODUCTION: Oral contraceptive pills have been implicated in the pathophysiology of breast cancer. Although many studies have examined the relationship between combined oral contraceptives (COCs) and breast cancer, there is a paucity of literature that discusses progestin-only oral contraceptives (POCs) and breast cancer. The purpose of this investigation is to examine potential associations between different types of oral contraceptives and breast cancer mortality in the South Carolina Medicaid population among different racial/ethnic groups. METHODS: Subjects included 4816 women diagnosed with breast cancer between 2000 and 2013. Kaplan-Meier curves were calculated to determine time-to-mortality rates among users of oral contraceptives. Competing-risks models and Cox multivariate survival models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of breast cancer and other-cause mortality, as well as all-cause mortality. RESULTS: POCs were associated with a significantly decreased risk of breast cancer mortality (HR: 0.07; 95% CI: 0.01, 0.52) and a non-significant increased risk of all-cause mortality (HR: 1.04; 95% CI: 0.52, 2.07). COCs increased the risk of breast cancer mortality (HR: 1.61; 95% CI: 1.14, 2.28) and all-cause mortality (HR: 1.83; 95% CI: 1.30, 2.57). CONCLUSION: Use of POCs may be associated with a decreased risk of breast cancer mortality. Due to the small sample size of POC users in the current study, additional research is needed to confirm these findings.

Intervention to Improve Quality of life for African-AmericaN lupus patients (IQAN): study protocol for a randomized controlled trial of a unique a la carte intervention approach to self-management of lupus in African Americans.

BACKGROUND: Systemic Lupus Erythematosus (lupus) is a chronic autoimmune disease that can impact any organ system and result in life-threatening complications. African-Americans are at increased risk for morbidity and mortality from lupus. Self-management programs have demonstrated significant improvements in health distress, self-reported global health, and activity limitation among people with lupus. Despite benefits, arthritis self-management education has reached only a limited number of people. Self-selection of program could improve such trends. The aim of the current study is to test a novel intervention to improve quality of life, decrease indicators of depression, and reduce perceived and biological indicators of stress in African-American lupus patients in South Carolina. METHODS/DESIGN: In a three armed randomized, wait list controlled trial, we will evaluate the effectiveness of a patient-centered 'a-la-carte' approach that offers subjects a variety of modes of interaction from which they can choose as many or few as they wish, compared to a 'set menu' approach and usual care. This unique 'a-la-carte' self-management program will be offered to 50 African-American lupus patients participating in a longitudinal observational web-based SLE Database at the Medical University of South Carolina. Each individualized intervention plan will include 1-4 options, including a mail-delivered arthritis kit, addition and access to an online message board, participation in a support group, and enrollment in a local self-management program. A 'set menu' control group of 50 lupus patients will be offered a standardized chronic disease self-management program only, and a control group of 50 lupus patients will receive usual care. Outcomes will include changes in (a) health behaviors, (b) health status, (c) health care utilization, and (d) biological markers (urinary catecholamines). DISCUSSION: Such a culturally sensitive educational intervention which includes self-selection of program components has the potential to improve disparate trends in quality of life, disease activity, depression, and stress among African-American lupus patients, as better outcomes have been documented when participants are able to choose/dictate the content and/or pace of the respective treatment/intervention program. Since there is currently no "gold standard" self-management program specifically for lupus, this project may have a considerable impact on future research and policy decisions. TRIAL REGISTRATION: NCT01837875 ; April 18, 2013.