Characterization of the primary antibody response to Plasmodium falciparum antigens in infants living in a malaria-endemic area.

BACKGROUND: The primary antibody (Ab) response to Plasmodium falciparum is a critical step in developing immunity to malaria. Information on the initial Ab responses of babies in malaria-endemic areas is incomplete, in part, because babies receive maternal IgG via transplacental-transfer and usually become infected before maternal IgG wanes. The study aimed to identify the primary IgM and IgG Ab responses to malarial antigens in Cameroonian babies. METHODS: Infants (n = 70) living in a high malaria transmission area were followed from birth throughout the first year of life (mean 341 ± 42 days, an average of 8.5 time points per infant). Malaria infection was assessed by microscopy and PCR, and IgM and IgG antibodies (Abs) were measured using a multiplex immunoassay to AMA1, EBA-175, MSP1-42, MSP2, MSP3, RESA, LSA1, and CSP. RESULTS: The half-life of maternal IgG varied among the antigens, ranging from 0.7 to 2.5 months. The first infection of 41% of the babies was sub-microscopic and only 11 to 44% of the babies produced IgM to the above antigens; however, when the first infection was detected by microscopy, 59-82% of the infants made IgM Abs to the antigens. Infants were able to produce IgM even when maternal IgG was present, suggesting maternal Abs did not suppress the baby's initial Ab response. Using longitudinal regression models that incorporated time-varying covariates, infants were found to produce IgG Ab to only AMA-1 when the first infection was sub-microscopic, but they produced IgG Abs to MSP1-42 (3D7, FVO), AMA1 (3D7, FVO) MSP2-FC27, MSP3, RESA, and LSA1, but not MSP 2-3D7, EBA-175, and CSP during their first slide-positive infection. Notably, the primary and secondary IgG responses were short-lived with little evidence of boosting. CONCLUSIONS: The primary Ab response of babies who had maternal IgG was similar to that reported for primary infections in malaria-naïve adults.

Quantiles based personalized treatment selection for multivariate outcomes and multiple treatments.

In this work, we propose a method for individualized treatment selection when there are correlated multiple responses for the K treatment ( K≥2 ) scenario. Here we use ranks of quantiles of outcome variables for each treatment conditional on patient-specific scores constructed from collected covariate measurements. Our method covers any number of treatments and outcome variables using any number of quantiles and it can be applied for a broad set of models. We propose a rank aggregation technique for combining several lists of ranks where both these lists and elements within each list can be correlated. The method has the flexibility to incorporate patient and clinician preferences into the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples. We also present illustrations using two different datasets from diabetes and HIV-1 clinical trials to show the applicability of the proposed procedure for real data.

Racial/ethnic differences in health-related quality of life among Hawaii adult population.

BACKGROUND: This study examined racial/ethnic differences in health-related quality of life (HRQOL) among adults and identified variables associated with HRQOL by race/ethnicity. METHODS: This study was conducted under a cross-sectional design. We used the 2011-2016 Hawaii Behavioral Risk Factor Surveillance System data. HRQOL were assessed by four measures: self-rated general health, physically unhealthy days, mentally unhealthy days, and days with activity limitation. Distress was defined as fair/poor for general health and 14 days or more for each of the other three HRQOL measures. We conducted multivariable logistic regressions with variables guided by Anderson's behavioral model on each distress measure by race/ethnicity. RESULTS: Among Hawaii adults, 30.4% were White, 20.9% Japanese, 16.8% Filipino, 14.6% Native Hawaiian and Pacific Islander (NHPI), 5.9% Chinese, 5.2% Hispanics, and 6.2% Other. We found significant racial/ethnic differences in the HRQOL measures. Compared to Whites, Filipinos, Japanese, NHPIs, and Hispanics showed higher distress rates in general health, while Filipinos and Japanese showed lower distress rates in the other HRQOL measures. Although no variables were consistently associated with all four HRQOL measures across all racial/ethnic groups, history of diabetes were significantly associated with general health across all racial/ethnic groups and history of depression was associated with at least three of the HRQOL measure across all racial/ethnic groups. CONCLUSIONS: This study contributes to the literature on disparities in HRQOL and its association with other variables among diverse racial/ethnic subgroups. Knowing the common factors for HRQOL across different racial/ethnic groups and factors specific to different racial/ethnic groups will provide valuable information for identifying future public health priorities to improve quality of life and reduce health disparities.

Selection of the optimal personalized treatment from multiple treatments with multivariate outcome measures.

In this work, we propose a novel method for individualized treatment selection when the treatment response is multivariate. For the K treatment (K ≥2) scenario we compare quantities that are suitable indexes based on outcome variables for each treatment conditional on patient-specific scores constructed from collected covariate measurements. Our method covers any number of treatments and outcome variables, and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique to estimate an ordering of treatments based on ranked lists of treatment performance measures such as smooth conditional means and conditional probability of a response for one treatment dominating others. The method has the flexibility to incorporate patient and clinician preferences to the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples. We also present data analyses using HIV and Diabetes clinical trials data to show the applicability of the proposed procedure for real data.

Personalized treatment plans with multivariate outcomes.

In this work, we propose a novel method for individualized treatment selection when the treatment response is multivariate. Our method covers any number of treatments and it can be applied for a broad set of models. The proposed method uses a Mahalanobis-type distance measure to establish an ordering of treatments based on treatment performance measures. Our investigation in this work deals with means of responses conditional on lower dimensional composite scores based on covariates where these scores are built using single index models to approximate mean responses against patient covariates. Smoothed estimates of such conditional means are combined to construct an estimate of the aforementioned distance measure, which is then used to estimate the optimal treatment. An empirical study demonstrates the performance of the proposed method in finite samples. We also present a data analysis using an HIV clinical trial data to show the applicability of the proposed procedure for real data.

Development of a miRNA-seq based prognostic signature in lung adenocarcinoma.

BACKGROUND: We utilized miRNAs expression and clinical data to develop a prognostic signature for patients with lung adenocarcinoma, with respect to their overall survival, to identify high-risk subjects based on their miRNA genomic profile. METHODS: MiRNA expressions based on miRNA sequencing and clinical data of lung adenocarcinoma patients (n = 479) from the Cancer Genome Atlas were randomly partitioned into non-overlapping Model (n = 320) and Test (n = 159) sets, respectively, for model estimation and validation. RESULTS: Among the ten miRNAs identified using the univariate Cox analysis, six from miR-8, miR-181, miR-326, miR-375, miR-99a, and miR-10, families showed improvement of the overall survival chance, while two miRNAs from miR-582 and miR-584 families showed a worsening of survival chances. The final prognostic signature was developed with five miRNAs-miR-375, miR-582-3p, miR-326, miR-181c-5p, and miR-99a-5p-utilizing a stepwise variable selection procedure. Using the KEGG pathway analysis, we found potential evidence supporting their significance in multiple cancer pathways, including non-small cell lung cancer. We defined two risk groups with a score calculated using the Cox regression coefficients. The five-year survival rates for the low-risk group was approximately 48.76% (95% CI = (36.15, 63.93)); however, it was as low as 7.50% (95% CI = (2.34, 24.01)) for the high-risk group. Furthermore, we demonstrated the effect of the genomic profile using the miRNA signature, quantifying survival rates for hypothetical subjects in different pathological stages of cancer. CONCLUSIONS: The proposed prognostic signature can be used as a reliable tool for identifying high-risk subjects regarding survival based on their miRNA genomic profile.

Personalized treatment selection using data from crossover designs with carry-over effects.

In this work, we propose a semiparametric method for estimating the optimal treatment for a given patient based on individual covariate information for that patient when data from a crossover design are available. Here, we assume there are carry-over effects for patients switching from one treatment to another. For the K treatment (K ≥ 2) scenario, we show that nonparametric estimation of carry-over effects can have the undesirable property that comparison of treatment means can only be done using independent outcome measurements from different groups of patients rather than using available joint measurements for each patient. To overcome this barrier, we compare probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient-specific scores constructed from patient covariates. We suggest single-index models as appropriate models connecting outcome variables to covariates and our empirical investigations show that frequencies of correct treatment assignments are highly accurate. The proposed method is also rather robust against departures from a single-index model structure. We also conduct a real data analysis to show the applicability of the proposed procedure.

Racial and ethnic differences in mental health service utilization among the Hawaii medicaid population.

Background: Racial/ethnic differences have not been adequately addressed in the utilization of mental health services among Medicaid populations. Aim: This study aimed to examine racial/ethnic differences in the utilization of health services for mental disorders in a Medicaid adult population aged between 21 and 64 years. Methods: Racial/ethnic differences in inpatient, outpatient and emergency department services utilization were assessed using 2010 Hawaii Medicaid data. Zero-inflated negative binomial regressions were employed adjusting for age, sex and residential area. Results: Among 73,200 beneficiaries, 29.7% were Whites, 28.5% Asians, 34.7% Native Hawaiians and Pacific Islanders (NHPIs); 60.2% were younger (21-44 years) and 58.6% were females. The patterns of utilization of health services differed across race/ethnicity. Compared to Whites, Asians and NHPIs were less likely to use outpatient services and had lower rates of inpatient visits. NHPIs were also estimated to have lower rates of outpatient and emergency department visits. Conclusion: Variation in the utilization of health services emphasizes the importance of race/ethnicity in mental health management. Various factors, such as language barriers and cultural differences, should be considered in developing clinical interventions or integrative health programs that aim to reduce racial/ethnic disparities among people with mental disorders.

Progression of diabetes, ischemic heart disease, and chronic kidney disease in a three chronic conditions multistate model.

BACKGROUND: Diabetes mellitus, ischemic heart disease, and chronic kidney disease are three major chronic conditions that develop with increasing risks among adults as they get older. The interconnectedness of these three chronic conditions is well known, while each condition acts as a prognostic risk factor for the other two. It is important to understand the progressive relationships of these three conditions over time in terms of transitioning between clinical states and the impact on patients' survival. METHODS: We investigate the survival characteristics of a Medicare population aged 65 years and above in a multistate system that contained clinical states specified by death and diagnosis combinations of three chronic conditions. The study was conducted using Hawaii Medicare claims data from 2009 to 2013. To evaluate the progression of a subject with one of the newly diagnosed chronic conditions, we analyzed quantities such as state occupation probabilities in eight states and hazards of sixteen transition types. We quantified effects and significances of potential covariates such as age, gender, race/ethnicity, comorbidity burden and financial status on these temporal functions. Nonparametric method of estimating state occupation probabilities and pseudo-value based method for estimating covariate effects of a survival system were utilized. RESULTS: We found a range of age, gender, race/ethnicity and financial status based interesting covariate influences on transitions and state occupation probabilities of the system. CONCLUSION: Survival characteristics of the disease system are influenced by subject-specific effects. Subgroup-specific interventions/screenings should be considered for the optimal prevention and care.

Flexible semi-parametric regression of state occupational probabilities in a multistate model with right-censored data.

Inference for the state occupation probabilities, given a set of baseline covariates, is an important problem in survival analysis and time to event multistate data. We introduce an inverse censoring probability re-weighted semi-parametric single index model based approach to estimate conditional state occupation probabilities of a given individual in a multistate model under right-censoring. Besides obtaining a temporal regression function, we also test the potential time varying effect of a baseline covariate on future state occupation. We show that the proposed technique has desirable finite sample performances and its performance is competitive when compared with three other existing approaches. We illustrate the proposed methodology using two different data sets. First, we re-examine a well-known data set dealing with leukemia patients undergoing bone marrow transplant with various state transitions. Our second illustration is based on data from a study involving functional status of a set of spinal cord injured patients undergoing a rehabilitation program.

Statistical prediction of immunity to placental malaria based on multi-assay antibody data for malarial antigens.

BACKGROUND: Plasmodium falciparum infections are especially severe in pregnant women because infected erythrocytes (IE) express VAR2CSA, a ligand that binds to placental trophoblasts, causing IE to accumulate in the placenta. Resulting inflammation and pathology increases a woman's risk of anemia, miscarriage, premature deliveries, and having low birthweight (LBW) babies. Antibodies (Ab) to VAR2CSA reduce placental parasitaemia and improve pregnancy outcomes. Currently, no single assay is able to predict if a woman has adequate immunity to prevent placental malaria (PM). This study measured Ab levels to 28 malarial antigens and used the data to develop statistical models for predicting if a woman has sufficient immunity to prevent PM. METHODS: Archival plasma samples from 1377 women were screened in a bead-based multiplex assay for Ab to 17 VAR2CSA-associated antigens (full length VAR2CSA (FV2), DBL 1-6 of the FCR3, 3D7 and 7G8 lines, ID1-ID2a (FCR3 and 3D7) and 11 antigens that have been reported to be associated with immunity to P. falciparum (AMA-1, CSP, EBA-175, LSA1, MSP1, MSP2, MSP3, MSP11, Pf41, Pf70 and RESA)). Ab levels along with clinical variables (age, gravidity) were used in the following seven statistical approaches: logistic regression full model, logistic regression reduced model, recursive partitioning, random forests, linear discriminant analysis, quadratic discriminant analysis, and support vector machine. RESULTS: The best and simplest model proved to be the logistic regression reduced model. AMA-1, MSP2, EBA-175, Pf41, and MSP11 were found to be the top five most important predictors for the PM status based on overall prediction performance. CONCLUSIONS: Not surprising, significant differences were observed between PM positive (PM+) and PM negative (PM-) groups for Ab levels to the majority of malaria antigens. Individually though, these malarial antigens did not achieve reasonably high performances in terms of predicting the PM status. Utilizing multiple antigens in predictive models considerably improved discrimination power compared to individual assays. Among seven different classifiers considered, the reduced logistic regression model produces the best overall predictive performance.

Selection of the optimal personalized treatment from multiple treatments with right-censored multivariate outcome measures.

We propose a novel personalized concept for the optimal treatment selection for a situation where the response is a multivariate vector that could contain right-censored variables such as survival time. The proposed method can be applied with any number of treatments and outcome variables, under a broad set of models. Following a working semiparametric Single Index Model that relates covariates and responses, we first define a patient-specific composite score, constructed from individual covariates. We then estimate conditional means of each response, given the patient score, correspond to each treatment, using a nonparametric smooth estimator. Next, a rank aggregation technique is applied to estimate an ordering of treatments based on ranked lists of treatment performance measures given by conditional means. We handle the right-censored data by incorporating the inverse probability of censoring weighting to the corresponding estimators. An empirical study illustrates the performance of the proposed method in finite sample problems. To show the applicability of the proposed procedure for real data, we also present a data analysis using HIV clinical trial data, that contained a right-censored survival event as one of the endpoints.

Optimal Personalized Treatment Selection with Multivariate Outcome Measures in a Multiple Treatment Case.

In this work we propose a novel method for individualized treatment selection when there are correlated multiple treatment responses. For the K treatment (K ≥ 2) scenario, we compare quantities that are suitable indexes based on outcome variables for each treatment conditional on patient-specific scores constructed from collected covariate measurements. Our method covers any number of treatments and outcome variables, and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique that takes into account possible correlations among ranked lists to estimate an ordering of treatments based on treatment performance measures such as the smooth conditional mean. The method has the flexibility to incorporate patient and clinician preferences into the optimal treatment decision on an individual case basis. A simulation study demonstrates the performance of the proposed method in finite samples. We also present data analyses using HIV clinical trial data to show the applicability of the proposed procedure for real data.

Racial/Ethnic Disparities in the Alzheimer's Disease Link with Cardio and Cerebrovascular Diseases, Based on Hawaii Medicare Data.

Background: There is an expanding body of literature implicating heart disease and stroke as risk factors for Alzheimer's disease (AD). Hawaii is one of the six majority-minority states in the United States and has significant racial health disparities. The Native-Hawaiians/Pacific-Islander (NHPI) population is well-known as a high-risk group for a variety of disease conditions. Objective: We explored the association of cardiovascular disease with AD development based on the Hawaii Medicare data, focusing on racial disparities. Methods: We utilized nine years of Hawaii Medicare data to identify subjects who developed heart failure (HF), ischemic heart disease (IHD), atrial fibrillation (AF), acute myocardial infarction (AMI), stroke, and progressed to AD, using multistate models. Propensity score-matched controls without cardiovascular disease were identified to compare the risk of AD after heart disease and stroke. Racial/Ethnic differences in progression to AD were evaluated, accounting for other risk factors. Results: We found increased risks of AD for AF, HF, IHD, and stroke. Socioeconomic (SE) status was found to be critical to AD risk. Among the low SE group, increased AD risks were found in NHPIs compared to Asians for all conditions selected and compared to whites for HF, IHD, and stroke. Interestingly, these observations were found reversed in the higher SE group, showing reduced AD risks for NHPIs compared to whites for AF, HF, and IHD, and to Asians for HF and IHD. Conclusions: NHPIs with poor SE status seems to be mostly disadvantaged by the heart/stroke and AD association compared to corresponding whites and Asians.

Limited English Proficiency, Postoperative Complications, and Interpreter Use in Vascular Surgery Patients in Hawai'i.

A retrospective cohort analysis of inpatient and outpatient vascular surgery cases from 2014 to 2018 was conducted to analyze the relationship between limited English proficiency (LEP) and undesirable postoperative outcomes, and to evaluate interpreter use as part of culturally and linguistically appropriate services (CLAS). Propensity score matching and logistic regression models were used to examine the association of English proficiency with postoperative outcomes and chart review was done to examine CLAS provision. Of the 959 cases, 57 (6%) were LEP and had noticeably worse health status before surgery than non-LEP. The 57 cases include 51 patients who had a single vascular surgery and 3 patients who had 2 vascular surgeries (different medical encounter/visit). There was no statistically significant difference in postoperative outcomes between patients with LEP and without LEP. Males with LEP were significantly less likely than females to receive CLAS (P=.008). On the day of vascular surgery and/or the day informed surgical consent was obtained, 16% of patients with LEP received access to interpreters; 25% had no documentation about interpreter provision, and 59% had mixed language access (family, staff, or interpreter). The provision of interpreters might be influenced by providers' perceived ability to communicate with patients with LEP without an interpreter, ease of obtaining an interpreter, availability of family or ad-hoc interpreters, and patients' preferences. Future research should examine reasons for frequent use of untrained individuals and inform strategies to implement language services in line with national standards.

Stepwise Proportional Weighting Algorithm for Single-Race Population Estimation Using Hawai'i Census Data.

Many health and health disparities studies require population prevalence information of various race groups, but the estimation of single-race population sizes using the US Census data has been challenging. For each Census race group, Census only provides the counts of those reported being single race ("race alone") and those reported of that specific race regardless of whether the individuals were multiracial or not ("race alone or in (any) combination"). The issue of how to classify Census multiracial individuals is especially important for the state of Hawai'i due to its large multiracial population. The current study developed the Stepwise Proportional Weighting Algorithm (SPWA) for single-race population estimation using US Census data for major race groups in the Census and their nested detailed races. Additionally, given that "partial Native Hawaiian" has often been treated as "Native Hawaiian" in health disparities studies in Hawai'i, the algorithm can also adjust for the unique partial Native Hawaiian race categorization. This paper describes the estimation process with the SPWA and demonstrates its ability to estimate single-races for the 5 most common race groups in Hawai'i. This new methodology addresses an important concern regarding how to classify multiracial individuals to strengthen health and health disparities research in Hawai'i.

Age and Sex Distributions of 31 Common Racial Groups in Hawai'i: A Shiny Web Application.

Hawai'i is the most ethnically diverse state with the highest proportion of multiracial individuals in the United States. The Stepwise Proportional Weighting Algorithm (SPWA) was developed to bridge the categorization of multiracial Census data into single-race population estimates for common races in Hawai'i. However, these estimates have not been publicly available. A Shiny web application, the Hawai'i Single-Race Categorization Tool, was developed as a user friendly research tool to obtain the age and sex distributions of single-race estimates for common racial groups in Hawai'i. The Categorization Tool implements the SPWA and presents the results in tabular and graphic formats, stratified by sex and age. It also allows the categorization of partial Native Hawaiians as Native Hawaiians in the population estimation. Using this tool, the current paper reports population estimates and distributions for 31 common racial groups using Hawai'i Census 2010 data. Among the major Census races, Asian had the largest population (631 881; 46.5%) in Hawai'i, followed by White (431 635; 31.7%) and Native Hawaiian and Other Pacific Islander (227 588; 16.7%). Among Census detailed races within Asian, Filipino had the largest population estimate (244 730; 18.0%), followed by Japanese (227 165; 16.7%) and Chinese (103 600; 7.6%). Native Hawaiian accounted for 12.3% of the Hawai'i population (166 944). After recategorizing part-Native Hawaiians as Native Hawaiians, Native Hawaiian increased by 150.0%, with the greatest increase among the young. This publicly available tool would be valuable for race-related resource allocation, policy development, and health disparities research in Hawai'i.

Perceived Barriers and Benefits of COVID-19 Testing among Pacific Islanders on Guam.

Pacific Islanders residing in the U.S. Affiliated Pacific Islands have had among the highest COVID-19-associated morbidity and mortality rates in the U.S. To reduce this disparity, we conducted a study to increase the reach and uptake of COVID-19 testing in Guam. Participants, who completed a pre-survey on demographics, health status, history of COVID-19 testing and vaccination, access to COVID-19 testing, sources of COVID-19 information, and knowledge and attitudes towards COVID-19 test results and transmission, were invited to attend an online educational session about COVID-19 testing and transmission and to complete a post-survey. There were significant positive changes between pre- and post-survey in knowledge and perceptions about COVID-19 testing and transmission, but changes were not necessarily due to exposure to the educational session. Compared to CHamoru participants (n = 380), Other Micronesians (n = 90) were significantly less knowledgeable about COVID-19 transmission and testing, were significantly more likely to not want to know if they had COVID-19, were more likely to believe if they did have COVID-19 there was not much that could be done for them, and that they would have difficulty in getting the needed healthcare. This study is another example of disparities in health knowledge and perceptions of certain Pacific Islander groups.

Increased transmigration of intermediate monocytes associated with atherosclerotic burden in people with HIV on antiretroviral therapy.

This study evaluated the association between the transmigration of monocyte subpopulations that contributes to atherosclerosis development, along with surrogate biomarkers of inflammation and atherosclerosis, through carotid intima-media thickness (cIMT) measurements of 72 people with HIV (PWH) on suppressive antiretroviral therapy (ART). We found that the transmigration of intermediate monocytes was positively correlated with D-dimer and cIMT, suggesting that intermediate monocytes may have a greater propensity to promote cardiovascular disease (CVD) in PWH on ART.

Impact of antiretroviral therapy intensification with C-C motif chemokine receptor 5 antagonist maraviroc on HIV-associated neurocognitive impairment.

OBJECTIVES: Chemokine receptor CCR5 is the principal co-receptor for entry of M-tropic HIV virus into immune cells. It is expressed in the central nervous system and may contribute to neuro-inflammation. The CCR5 antagonist maraviroc (MVC) has been suggested to improve HIV-associated neurocognitive impairment (NCI). DESIGN: A double-blind, placebo-controlled, 48-week, randomized study of MVC vs. placebo in people with HIV (PWH) on stable antiretroviral therapy (ART) for more than one year in Hawaii and Puerto Rico with plasma HIV RNA less than 50 copies/ml and at least mild NCI defined as an overall or domain-specific neuropsychological z (NPZ) score less than -0.5. METHODS: Study participants were randomized 2 : 1 to intensification of ART with MVC vs. placebo. The primary endpoint was change in global and domain-specific NPZ modeled from study entry to week 48. Covariate adjusted treatment comparisons of average changes in cognitive outcome were performed using winsorized NPZ data. Monocyte subset frequencies and chemokine expression as well as plasma biomarker levels were assessed. RESULTS: Forty-nine participants were enrolled with 32 individuals randomized to MVC intensification and 17 to placebo. At baseline, worse NPZ scores were seen in the MVC arm. Comparison of 48-week NPZ change by arm revealed no differences except for a modest improvement in the Learning and Memory domain in the MVC arm, which did not survive multiplicity correction. No significant changes between arms were seen in immunologic parameters. CONCLUSION: This randomized controlled study found no definitive evidence in favor of MVC intensification among PWH with mild cognitive difficulties.