Effect of olfactory bulb pathology on olfactory function in normal aging.

Decline of olfactory function is frequently observed in aging and is an early symptom of neurodegenerative diseases. As the olfactory bulb (OB) is one of the first regions involved by pathology and may represent an early disease stage, we specifically aimed to evaluate the contribution of OB pathology to olfactory decline in cognitively normal aged individuals without parkinsonism or dementia. This clinicopathological study correlates OB tau, amyloid ß (Aß) and α-synuclein (αSyn) pathology densities and whole brain pathology load to olfactory identification function as measured with the University of Pennsylvania Smell Identification Test (UPSIT) and clinical data measured proximate to death in a large autopsy study including 138 cases considered non-demented controls during life. Tau pathology was frequently observed in the OB (95% of cases), while both Aß (27% of cases) and αSyn (20% of cases) OB pathologies were less commonly observed. A weak correlation was only observed between OB tau and olfactory performance, but when controlled for age, neither OB tau, Aß or αSyn significantly predict olfactory performance. Moreover, whole brain tau and αSyn pathology loads predicted olfactory performance; however, only αSyn pathology loads survived age correction. In conclusion, OB tau pathology is frequently observed in normally aging individuals and increases with age but does not appear to independently contribute to age-related olfactory impairment suggesting that further involvement of the brain seems necessary to contribute to age-related olfactory decline.

Is diabetes associated with increased pathological burden in Alzheimer's disease?

INTRODUCTION: We examined the association between Alzheimer's disease (AD) and type 2 diabetes mellitus (DM) and hypothesized that diabetes is associated with an increased pathological burden in clinically and pathologically diagnosed AD. METHODS: All data were obtained from the Uniform Data Set (UDS) v3, the Neuropathology Data Set, and the Researcher's Data Dictionary-Genetic Data from the National Alzheimer's Coordinating Center. The dataset (37 cases with diabetes and 1158 cases without) relies on autopsy-confirmed data in clinically diagnosed AD patients who were assessed for diabetes type in form A5 or D2 during at least one visit. Differences in scores were explored using a general linear model. Effect sizes were calculated using sample means and standard deviations (Cohen's d). RESULTS: The presence of diabetes was associated with a lower Thal phase of amyloid plaques (A score; 4.6 ± 0.79 vs. 4.3 ± 0.85, P < .05) and lower Braak stage for neurofibrillary degeneration (B score; 5.58 ± 0.72 vs. 5.16 ± 0.96, P < 0.05) but not for density of neocortical neuritic plaques (CERAD score-C score). The National Institute on Aging-Alzheimer's Association Alzheimer's disease neuropathologic change (ABC score) was not different between AD+DM and AD-DM. DISCUSSION: This pilot study found a significantly lower Thal phase of amyloid plaques and Braak stage for neurofibrillary degeneration in AD-confirmed individuals with diabetes compared to those without. Thus type 2 DM is not associated with increased AD pathology in clinically and pathologically confirmed cases of AD.

A Systematic Review and Meta-analysis of Surgical Treatment of Ectopic Pregnancy with Salpingectomy versus Salpingostomy.

OBJECTIVE: To evaluate the fertility outcomes of salpingectomy compared with those of salpingostomy among patients treated for tubal ectopic pregnancies, including a separate analysis of women with risk factors along with a review of the surgical technique. DATA SOURCES: Systematic review and meta-analysis from 1990 to the present through PubMed, Embase, CINAHL, and Ovid MEDLINE. The search string included "tubal pregnancy" or "ectopic" as well as "salpingectomy" and various terms describing salpingotomy. METHODS OF STUDY SELECTION: Articles studying women who underwent surgical management of an ectopic pregnancy and the contrasted outcomes of salpingectomy vs salpingostomy were reviewed. The primary outcomes included subsequent intrauterine pregnancy (IUP) and repeat ectopic pregnancy (REP). TABULATION, INTEGRATION, AND RESULTS: Two randomized controlled trials (RCTs), which consisted mostly of patients classified as low risk, and patients from 16 cohort studies were included. In the RCTs, there was no significant difference in the odds of subsequent IUP in patients who underwent a salpingectomy compared with those who were treated with salpingotomy (odds ratio [OR] 0.97; 95% confidence interval [CI], 0.71-1.33). However, a significant and clinically meaningful difference was noted in the cohort studies, with the patients having a lower chance of IUP after salpingectomy (OR 0.45; 95% CI, 0.39-0.52). No significant difference was noted in the OR for a REP in the randomized trials (OR 0.77; 95% CI, 0.41-1.47), but the patients followed in the cohort studies had a cumulatively higher risk of REP after a salpingostomy (OR 0.73; 95% CI, 0.60-0.90). The subgroup analysis examining women within the studies with risk factors for tubal pathology found an even more impressive lowering in the odds of a subsequent IUP in patients classified as at-risk who were treated with salpingectomy (OR 0.30; 95% CI, 0.17-0.54), with a change in the direction of the odds for an REP rate favoring those who were treated with salpingostomy (OR 1.96; 95% CI, 0.88-4.35). CONCLUSION: Salpingectomy has clear advantages over salpingostomy, and RCTs consisting mainly of patients classified as low risk show no difference in outcomes between salpingectomy and salpingostomy. However, in cohort studies inclusive of all patients, the likelihood of a subsequent spontaneous IUP is decreased in patients treated with salpingectomy, and salpingostomies may be especially underused in women with risk factors for tubal disease.

Impacts on knowledge and testing on HIV in waves of Mozambique surveys with Bayes estimates.

BACKGROUND: It is well known that it is more reliable to investigate the effects of several covariates simultaneously rather than one at time. Similarly, it is more informative to model responses simultaneously, as more often than not, the multiple responses from the same subject are correlated. This is particularly true in the analysis of Mozambique survey data from 2009 and 2018. METHOD: A multiple response predictive model for testing positive for HIV and having sufficient HIV knowledge is modeled to 2009 and 2018 survey data with the use of Bayes estimates. These data are obtained through a hierarchical data structure. The model allows one to address the change in the response to HIV, as it relates to morbidity and to HIV knowledge in Mozambique in the fight against the disease in the last decade. RESULTS: A more affluent resident is more likely to test positive, more likely to be more knowledgeable about the disease. Whereas, individuals practicing the Islam faith are less likely to test positive but also less likely to be knowledgeable about the disease. Education, while still a factor, has declined in its impact on testing positive for HIV or being knowledgeable about HIV. Females are more likely to test positive but more likely to be knowledgeable about the disease than men. The rate of impact of affluence on knowledge has increased in the past decade. Marital status (cohabitating or married) showed no impact on the knowledge of the disease. Age had no impact on knowledge suggesting that the message is getting to resident. CONCLUSIONS: A joint Bayes modeling of correlated binary (testing positive and knowledge about the disease) responses, while accounting for the hierarchy of the data collection, presents an opportunity to extract the extra variation before allocating the variation on the responses as the due of the covariates. The fight against HIV in Mozambique seems to be succeeding. Some knowledge is common among all ages, and Islam religion has a positive effect. While education still shows an influence on the binary responses, it has declined over the last decade.

Analysis of correlated data with feedback for time-dependent covariates in psychiatry research.

In studies on psychiatry and neurodegenerative diseases, it is common to have data that are correlated due to the hierarchical structure in data collection or to repeated measures on the subject longitudinally. However, the feedback effect created due to time-dependent covariates in these studies is often overlooked and seldom modelled. This article reviews the methodological development of feedback effects with marginal models for longitudinal data and discusses their implementation.

HIV survey in Mozambique: analysis with simultaneous model in contrast to separate hierarchical models.

BACKGROUND: The analysis of correlated responses obtained one at a time in survey data is not as informative or as useful as modeling them simultaneously. Simultaneous modeling allows for the opportunity to evaluate the system in a more pragmatic form rather than to allow for responses that assumedly originated in isolation. METHODS: This research uses the Mozambique National Survey data to demonstrate the benefits of simultaneous modeling on blood test results, knowledge of HIV/AIDS, and awareness of an HIV/AIDS campaign. This simultaneous modeling also addresses the correlation inherent due to the hierarchical structure in the data collection. RESULTS: Employment and self-perceived risk of HIV/AIDS have different impact on blood test, awareness of an HIV/AIDS campaign, and knowledge of HIV/AIDS when examined simultaneously as opposed to separate modeling. CONCLUSION: Simultaneous modeling of correlated responses improves the reliability of the estimates. More importantly, it provides an opportunity to engage in cost-saving decisions when designing future surveys and make better health policies.

Use of partitioned GMM marginal regression model with time-dependent covariates: analysis of Chinese Longitudinal Healthy Longevity Study.

BACKGROUND: Elderly population's health is a major concern for most industrial nations. National health surveys provide a measure of the state of elderly health. One such survey is the Chinese Longitudinal Healthy Longevity Survey. It collects data on risk factors and outcomes on the elderly. We examine these longitudinal survey data to determine the changes in health and to identify risk factors as they impact health outcomes including the elderly's ability to do a physical check. METHODS: We use a Partitioned GMM logistic regression model to identify risk factors. The model also accounts for the correlation between lagged time-dependent covariates and the outcomes. It addresses present and past measures of time-dependent covariates on simultaneous outcomes. The relation produces additional regression coefficients as byproduct of the Partitioned model, identifying the immediate, delayed effects (lag - 1), further delayed (lag-2), etc. Therefore, the model presents the opportunity for decision makers to monitor the covariate over time. This technique is particularly useful in healthcare and health related research. We use the Chinese Longitudinal Health Longevity Survey data to identify those risk factors and to display the utility of the model. RESULTS: We found that one's ability to make own decisions, frequently consuming vegetables, exercise frequently, one's ability to transfer without assistance, having visual difficulties and being able to pick book from floor while standing had varying effects of significance on one's health and ability to complete physical checks as they get older. CONCLUSIONS: The partitioning of the covariates as immediate effect, delayed effect or further delayed effect are important measures in a declining population.

Bayesian multiple membership multiple classification logistic regression model on student performance with random effects in university instructors and majors.

Educational success measured by retention leading to graduation is an essential component of any academic institution. As such, identifying the factors that contribute significantly to success and addressing those factors that result in poor performances are important exercises. By success, we mean obtaining a semester GPA of 3.0 or better and a GPA of 2.0 or better. We identified these factors and related challenges through analytical models based on student performance. A large dataset obtained from a large state university over three consecutive semesters was utilized. At each semester, GPAs were nested within students and students were taking classes from multiple instructors and pursuing a specific major. Thus, we used multiple membership multiple classification (MMMC) Bayesian logistic regression models with random effects for instructors and majors to model success. The complexity of the analysis due to multiple membership modeling and a large number of random effects necessitated the use of Bayesian analysis. These Bayesian models identified factors affecting academic performance of college students while accounting for university instructors and majors as random effects. In particular, the models adjust for residency status, academic level, number of classes, student athletes, and disability residence services. Instructors and majors accounted for a significant proportion of students' academic success, and served as key indicators of retention and graduation rates. They are embedded within the processes of university recruitment and competition for the best students.

Modification of the generalized quasi-likelihood model in the analysis of the Add Health study.

The relationship between the mean and variance is an implicit assumption of parametric modeling. While many distributions in the exponential family have a theoretical mean-variance relationship, it is often the case that the data under investigation are correlated, thus varying from the relation. We present a generalized method of moments estimation technique for modeling certain correlated data by adjusting the mean-variance relationship parameters based on a canonical parameterization. The proposed mean-variance form describes overdispersion using two parameters and implements an adjusted canonical parameter which makes this approach feasible for all distributions in the exponential family. Test statistics and confidence intervals are used to measure the deviations from the mean-variance relation parameters. We use the modified relation as a means of fitting generalized quasi-likelihood models to correlated data. The performance of the proposed modified generalized quasi-likelihood model is demonstrated through a simulation study and the importance of accounting for overdispersion is highlighted through the evaluation of adolescent obesity data collected from a U.S. longitudinal study.

Split bootstrap hierarchical modeling of antibiotics abuse in China.

In the 1990s, China experienced a high degree of antibiotics abuse, which resulted in increased drug resistance. As a result, the World Health Organization introduced a program for children under the age of 5 years who had an acute respiratory tract infection. We analyze the data pertaining to the treatment provided by doctors in several hospitals in China in order to understand the relationships in the data. The data are nested in a three-level hierarchical structure with small cluster sizes ranging from 2 to 10. While large sample theory provides a mechanism to construct confidence intervals and test hypotheses about regression coefficients, the estimation algorithms often fail to converge when they are applied to small cluster sizes. This paper presents a combination of the cluster bootstrap and primary unit splitting methods, called split bootstrap, which is a novel combination that can be used as an alternative when analyzing data pertaining to the abuse of antibiotics in China with small cluster sizes. The split bootstrap method provides accurate estimations with a minimal reduction in precision.

Partitioned GMM logistic regression models for longitudinal data.

Correlation is inherent in longitudinal studies due to the repeated measurements on subjects, as well as due to time-dependent covariates in the study. In the National Longitudinal Study of Adolescent to Adult Health (Add Health), data were repeatedly collected on children in grades 7-12 across four waves. Thus, observations obtained on the same adolescent were correlated, while predictors were correlated with current and future outcomes such as obesity status, among other health issues. Previous methods, such as the generalized method of moments (GMM) approach have been proposed to estimate regression coefficients for time-dependent covariates. However, these approaches combined all valid moment conditions to produce an averaged parameter estimate for each covariate and thus assumed that the effect of each covariate on the response was constant across time. This assumption is not necessarily optimal in applications such as Add Health or health-related data. Thus, we depart from this assumption and instead use the Partitioned GMM approach to estimate multiple coefficients for the data based on different time periods. These extra regression coefficients are obtained using a partitioning of the moment conditions pertaining to each respective relationship. This approach offers a deeper understanding and appreciation into the effect of each covariate on the response. We conduct simulation studies, as well as analyses of obesity in Add Health, rehospitalization in Medicare data, and depression scores in a clinical study. The Partitioned GMM methods exhibit benefits over previously proposed models with improved insight into the nonconstant relationships realized when analyzing longitudinal data.

Impact of the Presence of Select Cardiovascular Risk Factors on Cognitive Changes among Dementia Subtypes.

BACKGROUND: Studies have shown select associations between cardiovascular risk factors and dementia, but mostly focused on Alzheimer's Disease (AD). OBJECTIVE: We enhance these works by evaluating the relationship between the presence of cardiovascular risk factors and the rate of cognitive decline, measured using the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating Sum of Boxes (CDR-SUM) on four common dementia subtypes (AD, dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and vascular dementia (VaD), as well as non-demented elderly individuals (normal)). METHOD: We used generalized linear mixed models with random intercepts to account for correlation at the patient and center levels for each dementia subtype adjusting for time since initial visit, baseline cognitive score, age, and demographic factors. The cardiovascular risk factors evaluated included body mass index, diabetes, years of smoking, atrial fibrillation, hypertension, and hypercholesterolemia. RESULTS: Patients diagnosed with AD (n=1899), DLB (n=65), FTD (n=168), or VaD (n=13); or lacked cognitive impairment (normal) (n=3583) were evaluated using data from the National Alzheimer's Coordinating Centers. Cardiovascular risk factors were associated with select dementia subtypes including AD and FTD. Using MMSE and CDR-SUM, recent or active hypertension and hypercholesterolemia were associated with a slower cognitive decline for AD patients, while higher body mass index and years of smoking were associated with a slower cognitive decline for FTD patients. However, several cardiovascular factors demonstrated associations with more rapid cognitive decline. CONCLUSION: These results demonstrate disease specific associations and can provide clinicians guidance on predicted cognitive changes at the group level using information about cardiovascular risk factors.

Joint modeling of correlated binary outcomes: The case of contraceptive use and HIV knowledge in Bangladesh.

Recent advances in statistical methods enable the study of correlation among outcomes through joint modeling, thereby addressing spillover effects. By joint modeling, we refer to simultaneously analyzing two or more different response variables emanating from the same individual. Using the 2011 Bangladesh Demographic and Health Survey, we jointly address spillover effects between contraceptive use (CUC) and knowledge of HIV and other sexually transmitted diseases. Jointly modeling these two outcomes is appropriate because certain types of contraceptive use contribute to the prevention of HIV and STDs and the knowledge and awareness of HIV and STDs typically lead to protection during sexual intercourse. In particular, we compared the differences as they pertained to the interpretive advantage of modeling the spillover effects of joint modeling HIV and CUC as opposed to addressing them separately. We also identified risk factors that determine contraceptive use and knowledge of HIV and STDs among women in Bangladesh. We found that by jointly modeling the correlation between HIV knowledge and contraceptive use, the importance of education decreased. The HIV prevention program had a spillover effect on CUC: what seemed to be impacted by education can be partially contributed to one's exposure to HIV knowledge. The joint model revealed a less significant impact of covariates as opposed to both separate models and standard models. Additionally, we found a spillover effect that would have otherwise been undiscovered if we did not jointly model. These findings further suggested that the simultaneous impact of correlated outcomes can be adequately addressed for the commonality between different responses and deflate, which is otherwise overestimated when examined separately.

An extent of resection threshold for seizure freedom in patients with low-grade gliomas.

OBJECTIVE Seizures are the most common presenting symptom of newly diagnosed WHO Grade II gliomas (low-grade glioma [LGG]) and significantly impair quality of life. Although gross-total resection of LGG is associated with better seizure control, it remains unclear whether an extent of resection (EOR) "threshold" exists for long-term seizure control. Specifically, what proportion of FLAIR-positive tissue in patients with newly diagnosed LGG must be removed to achieve Engel Class I seizure freedom? To clarify the EOR threshold for long-term seizure control, the authors analyzed data from a consecutive series of patients with newly diagnosed LGG who presented with seizures and subsequently underwent microsurgical resection. METHODS The authors identified consecutive patients with newly diagnosed LGG who presented with seizures and were treated at the Barrow Neurological Institute between 2002 and 2012. Patients were dichotomized into those who were seizure free postoperatively and those who were not. The EOR was calculated by quantitative comparison of pre- and postoperative MRI. Univariate analysis of these 2 groups included the chi-square test and the Mann-Whitney U-test, and a multivariate logistic regression was constructed to predict the impact of multiple independent variables on the likelihood of postoperative seizure freedom. To determine a threshold of EOR that optimizes seizure freedom, a receiver operating characteristic curve was plotted and the optimal point of discrimination was determined. RESULTS Data from 128 patients were analyzed (male/female ratio 1.37:1; mean age 40.8 years). All 128 patients presented with seizures, usually generalized (n = 57, 44.5%) or simple partial (n = 57, 44.5%). The median EOR was 90.0%. Of 128 patients, 46 (35.9%) had 100% volumetric tumor resection, 64 (50.0%) had 90%-99% volumetric tumor resection, and 11 (8.6%) had 80%-89% volumetric tumor resection. Postoperatively, 105 (82%) patients were seizure free (Engel Class I); 23 (18%) were not (Engel Classes II-IV). The proportion of seizure-free patients increased in proportion to the EOR. Predictive variables included in the regression model were preoperative Karnofsky Performance Scale score, seizure type, time from diagnosis to surgery, preoperative number of antiepileptic drugs, and EOR. Only EOR significantly affected the likelihood of postoperative Engel Class I status (OR 11.5, 95% CI 2.4-55.6; p = 0.002). The receiver operating characteristic curve generated based on Engel Class I status showed a sensitivity of 0.65 and 1 - specificity of 0.175, corresponding to an EOR of 80%. CONCLUSIONS For adult patients with LGG who suffer seizures, the results suggest that seizure freedom can be attained when EOR > 80% is achieved. Improvements in both the proportion of seizure-free patients and the durability of seizure freedom were observed beyond this 80% threshold. Interestingly, this putative EOR seizure-freedom threshold closely approximates that reported for the overall survival benefit in newly diagnosed hemispheric LGGs, suggesting that a minimum level of residual tumor burden is necessary for both disease and symptomatic progression.

Impact of communities, health, and emotional-related factors on smoking use: comparison of joint modeling of mean and dispersion and Bayes' hierarchical models on add health survey.

BACKGROUND: The analysis of correlated binary data is commonly addressed through the use of conditional models with random effects included in the systematic component as opposed to generalized estimating equations (GEE) models that addressed the random component. Since the joint distribution of the observations is usually unknown, the conditional distribution is a natural approach. Our objective was to compare the fit of different binary models for correlated data in Tabaco use. We advocate that the joint modeling of the mean and dispersion may be at times just as adequate. We assessed the ability of these models to account for the intraclass correlation. In so doing, we concentrated on fitting logistic regression models to address smoking behaviors. METHODS: Frequentist and Bayes' hierarchical models were used to predict conditional probabilities, and the joint modeling (GLM and GAM) models were used to predict marginal probabilities. These models were fitted to National Longitudinal Study of Adolescent to Adult Health (Add Health) data for Tabaco use. RESULTS: We found that people were less likely to smoke if they had higher income, high school or higher education and religious. Individuals were more likely to smoke if they had abused drug or alcohol, spent more time on TV and video games, and been arrested. Moreover, individuals who drank alcohol early in life were more likely to be a regular smoker. Children who experienced mistreatment from their parents were more likely to use Tabaco regularly. CONCLUSIONS: The joint modeling of the mean and dispersion models offered a flexible and meaningful method of addressing the intraclass correlation. They do not require one to identify random effects nor distinguish from one level of the hierarchy to the other. Moreover, once one can identify the significant random effects, one can obtain similar results to the random coefficient models. We found that the set of marginal models accounting for extravariation through the additional dispersion submodel produced similar results with regards to inferences and predictions. Moreover, both marginal and conditional models demonstrated similar predictive power.