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Purpose: To examine the effectiveness of a community-based intensive multifactorial clinical intervention for patients with Type 2 diabetes, to evaluate the feasibility of achieving clinical targets for glycemic control in a community setting, and to identify factors that are predictive of glycemic control in this cohort (age, gender, disease duration, continuity of care, pharmacologic treatment, diabetes self-care and smoking status). Methods: Participants with Type 2 diabetes referred to the Diabetes Clinic following dissemination of the 2003 Clinical Practice Guidelines of Canadian Diabetes Association and who attended a minimum of two physician visits within a twelve month period were deemed eligible for participation. 70 patients were included in this retrospective study. Baseline and twelve month values for the following biomedical outcomes were collected via chart audit: BMI, hemoglobin A1c, blood pressure (systolic, diastolic) and lipid profile (HDL, LDL, triglycerides, total cholesterol, TC:HDL ratio). Data for identification of predictive factors for glycemic control were also retrieved by chart audit. Results: The results of the paired t-test yielded a significant improvement in hemoglobin A1c (p<0.05), systolic blood pressure (p<0.01), HDL-cholesterol (p<0.05), LDL-cholesterol (p<0.01), total cholesterol (p<0.05) and total cholesterol:HDL ratio (p<0.05) over twelve months. No significant difference in BMI, diastolic blood pressure or triglycerides was reported over twelve months. Over half the sample (52.9%) achieved clinical targets for glycemic control (hemoglobin A1c <7.0%) at twelve months. Logistic regression analysis identified disease duration (O.R. = 0.90, 95% CI Exp(B) = 0.079 - 0.773, p = 0.01) and continuity of care (O.R. = 0.25, 95% CI Exp(B) = 0.831 - 0.969, p = 0.02) as significant predictors of glycemic control at twelve months. Conclusions: These findings demonstrate the effectiveness of this community-based intensive multifactorial clinical intervention for patients with Type 2 diabetes and show that the implementation of CPGs related to glycemic control is feasible in a community-based setting. Additionally, patients in this cohort with increased disease duration and increased continuity of care were less likely to achieve clinical targets for glycemic control following a twelve month intensive multifactorial clinical intervention for Type 2 diabetes. In summary, health professionals should strive to implement similar intensive multifactorial interventions in community practice in order to decrease the likelihood of diabetes-related complications and improve the patients quality of life.

Purpose: To examine the effectiveness of a community-based intensive multifactorial clinical intervention for patients with Type 2 diabetes, to evaluate the feasibility of achieving clinical targets for glycemic control in a community setting, and to identify factors that are predictive of glycemic control in this cohort (age, gender, disease duration, continuity of care, pharmacologic treatment, diabetes self-care and smoking status). Methods: Participants with Type 2 diabetes referred to the Diabetes Clinic following dissemination of the 2003 Clinical Practice Guidelines of Canadian Diabetes Association and who attended a minimum of two physician visits within a twelve month period were deemed eligible for participation. 70 patients were included in this retrospective study. Baseline and twelve month values for the following biomedical outcomes were collected via chart audit: BMI, hemoglobin A1c, blood pressure (systolic, diastolic) and lipid profile (HDL, LDL, triglycerides, total cholesterol, TC:HDL ratio). Data for identification of predictive factors for glycemic control were also retrieved by chart audit. Results: The results of the paired t-test yielded a significant improvement in hemoglobin A1c (p<0.05), systolic blood pressure (p<0.01), HDL-cholesterol (p<0.05), LDL-cholesterol (p<0.01), total cholesterol (p<0.05) and total cholesterol:HDL ratio (p<0.05) over twelve months. No significant difference in BMI, diastolic blood pressure or triglycerides was reported over twelve months. Over half the sample (52.9%) achieved clinical targets for glycemic control (hemoglobin A1c <7.0%) at twelve months. Logistic regression analysis identified disease duration (O.R. = 0.90, 95% CI Exp(B) = 0.079 - 0.773, p = 0.01) and continuity of care (O.R. = 0.25, 95% CI Exp(B) = 0.831 - 0.969, p = 0.02) as significant predictors of glycemic control at twelve months. Conclusions: These findings demonstrate the effectiveness of this community-based intensive multifactorial clinical intervention for patients with Type 2 diabetes and show that the implementation of CPGs related to glycemic control is feasible in a community-based setting. Additionally, patients in this cohort with increased disease duration and increased continuity of care were less likely to achieve clinical targets for glycemic control following a twelve month intensive multifactorial clinical intervention for Type 2 diabetes. In summary, health professionals should strive to implement similar intensive multifactorial interventions in community practice in order to decrease the likelihood of diabetes-related complications and improve the patients quality of life.

LED and HS have been funded by startup funds from Stanford and a pilot grant to LED from the Stanford Center for Clinical and Translation Research and Education (UL1 TR001085, PI Greenberg). LED has also been funded by Cohen Veterans Bioscience (CVB), and she is part of the CVB Working Group for PTSD Adaptive Platform Trial. BG has been funded by the NIH (R01-HD-059835, PI Williams) and CVB. HH has been funded by the NIH (NIH K01DK114379 and NIH R21AI139012), the Zhengxu and Ying He Foundation, and the Stanley Center for Psychiatric Research. MBR received funds from WPA Congress Mexico City 2018, Guayaquil CEPAM 2019, Asunción X CONGRESO LATINOAMERICANO DE LA FLAPB 2018, Guayaquil 2019 (Bago), and Lancet Psychiatry, London (commission on Violence against women) 2019. SS declares no potential conflict of interest. / Genome-wide approaches including polygenic risk scores (PRSs) are now widely used in medical research; however, few studies have been conducted in low- and middle-income countries (LMICs), especially in South America. This study was designed to test the transferability of psychiatric PRSs to individuals with different ancestral and cultural backgrounds and to provide genome-wide association study (GWAS) results for psychiatric outcomes in this sample. The PrOMIS cohort (N = 3308) was recruited from prenatal care clinics at the Instituto Nacional Materno Perinatal (INMP) in Lima, Peru. Three major psychiatric outcomes (depression, PTSD, and suicidal ideation and/or self-harm) were scored by interviewers using valid Spanish questionnaires. Illumina Multi-Ethnic Global chip was used for genotyping. Standard procedures for PRSs and GWAS were used along with extra steps to rule out confounding due to ancestry. Depression PRSs significantly predicted depression, PTSD, and suicidal ideation/self-harm and explained up to 0.6% of phenotypic variation (minimum p = 3.9 × 10−6). The associations were robust to sensitivity analyses using more homogeneous subgroups of participants and alternative choices of principal components. Successful polygenic prediction of three psychiatric phenotypes in this Peruvian cohort suggests that genetic influences on depression, PTSD, and suicidal ideation/self-harm are at least partially shared across global populations. These PRS and GWAS results from this large Peruvian cohort advance genetic research (and the potential for improved treatments) for diverse global populations. / National Institutes of Health / Revisión por pares

In biology and bioinformatics, a variety of data share a common property that challenges numerous cutting-edge research studies: heterogeneities at the individual level with respect to more than one factor. Examples of such heterogeneities include but are not limited to: 1) unequal susceptibility of different patients, and 2) large diversity in gene length, GC content, etc., along with the resulting gene characteristics. For many biological data analysis studies, the critical first step is usually to infer null probability distribution of observed data with the heterogeneities in multiple (confounding) factors taken into account, so that we can further investigate the impact of other factor(s) of interest. Obviously, the heterogeneities heavily influence the potential conclusions that we may draw from statistical analyses of the data. However, modeling such heterogeneities has been challenging, not only due to the inapplicable explicit modeling of all factors with heterogeneous effects on the data, but also because of the non-independence of many factors from one another. Existing methods, either partially/fully neglected the heterogeneity issue at all, or took care of each factor's heterogeneity in isolation. Evidences have shown the insufficiency of such strategies and the errors they may produce in downstream analyses. The emergence of large-scale data sets provides the opportunity to directly and comprehensively learn the heterogeneity from the data without explicitly modeling the mechanisms behind or exerting strong assumptions. The data, as often stored or organized as multidimensional contingency tensors, lead to a natural perspective of modeling heterogeneity with each impact factor of interest being one dimension. The heterogeneity in each factor's impact on the variable of interest can be captured by the marginal property of the data tensor with respect to the corresponding dimension. For instance, in a single-cell sequencing dataset, which can be organized as a matrix with each row representing a gene and each column representing a cell, the heterogeneity caused by both the gene and cell factors can be modeled. In this dissertation, we develop a novel model, Conditional Multifactorial Contingency (CMC), that models the intertwined heterogeneities in all dimensions of the data tensor and infers the probability distribution of each entry of the data tensor jointly conditioned on these heterogeneities. In the proposed CMC model, the problem is formulated as a maximum entropy problem of the contingency tensor's probability distribution subject to the marginal constraints, under the assumption that the individuals within each dimension are independent. The marginal constraints are applied to the expected value instead of observed trial outcomes, which plays a key role in avoiding the innumerable combinations of trial outcomes and leading to an elegant expression form of the entry's probability distribution. The model is first developed for 3D binary data matrix, then extended to multidimensional data tensors and integer data tensors. Furthermore, missing values are taken into account and CMC is extended to be compatible with data with missing values. Being empowered by CMC, we conducted four case studies for real-world bioinformatics research problems: (1) driving transcription factor (TF) identification; (2) scRNA-seq data normalization; (3) cancer-associated gene identification; (4) cell similarity quantification. For each of these case studies, we proposed a whole analysis framework and specific adaptation design for CMC. For the driving-TF identification, compared with traditional methods, we considered the variations in the gene's binding affinity in addition to the typically considered variations in TF's binding affinity. The driving TFs were identified by comparing the observed binding state and the estimated binding probability conditioned on TF/gene binding affinities. For the scRNA-seq data normalization, besides gene factor and cell factor, we figured out one more factor impacting the read counts, cDNA length, and applied CMC to comprehensively analyze the three factors. For cancer-associated gene identification, the CMC model is applied to systematically model the patient, gene, and mutation type factors in the mutation count data. As for the last application, to the best of our knowledge, our solution is the first proposed cell-to-cell-type similarity quantification method, thanks to the availability of CMC to systematically model and remove the impact of cell and gene factors. We studied the theoretical properties of the proposed model and validated the effectiveness and efficiency of our method through experiments. The uniqueness of the probability solution and the convergence of the algorithm was proved. In the endeavor to identify true driving TFs, CMC significantly boosted the best record of success rate, which was proved using data with ground truth. Besides, in an exploratory study without ground truth, in addition to the previously known TFs, Olig1 (ranks 2nd), Olig2 (ranks 3rd), and Sox10 (ranks 4th), we successfully identified Ppp1r14b (ranks 1st) and Zfp36l1 (ranks 6th) that function in oligodendrocyte lineage development, which was validated via biological knock-out experiments and, has led to genuine biological discoveries. In the scRNA-seq data normalization, experimental results show that, by taking the cell, gene, and cDNA-length factors into account, the normalized data achieves lower variances for housekeeping genes than the peer methods. Besides, the data normalized by the CMC model leads to better accuracy of downstream DEG detection than that normalized by peer normalization methods. In cancer-associated gene identification, the CMC model is able to eliminate most of the likely artefactual findings resulted by considering the hidden factors separately. In the cell similarity quantification, CMC based model enables the identification of cell types by establishing between-species cell similarity quantification, regardless of contamination in scRNA-seq data. / Doctor of Philosophy / Biological data are complicated and typically influenced by numerous factors, including characteristics of biological subjects, physical or chemical properties of molecules, artifacts created by experimental operations, and so on. The information of real interest in a biology/bioinformatics study can be buried in all sorts of irrelevant factors and their impacts on the data. Consider a simple example where a study is conducted to figure out if an association exists between a specific gene and a cancer. Although this gene shows obviously different frequencies of mutation in two groups of people, patients and the normal, we cannot safely confirm the association from this observation. Such differential mutation levels can also be a result of the diversity among all these people in how easily this gene is mutated in a person (related to many characteristics of this person besides "cancer/not"). We call this diversity "heterogeneity", and it actually can be seen everywhere, in people, in genes, in cells, and in cell types, etc. One needs to take good care of such heterogeneities so as to draw firm statistical hence scientific conclusions. However, handling the heterogeneities is far from trivial. On the one hand, it is generally impossible to fully understand the mechanisms behind those diversities, let alone to explicitly and rigorously formulate them. One the other hand, it is not rare that multiple factors intertwine with one another, in which case all these factors must be considered systematically in order to model the data precisely. Existing methods, either partially/fully neglected the heterogeneity issue at all, or took care of each factor's heterogeneity in isolation. Evidences have shown the insufficiency of such strategies and the errors they may produce in downstream analyses. As the exact mechanisms behind heterogeneities are usually not available, we aim to learn and infer the heterogeneities' effects on data from data itself. A large group of biological data can be stored or organized as multidimensional contingency tensors, with each impact factor of interest being one dimension. The heterogeneity in each factor's impact on the variable of interest can be captured by the marginal property of the data tensor with respect to the corresponding dimension, for example, the row sum and the column sum in a 2D tensor. In this dissertation, under the assumption that the individuals of each dimension are independent, we proposed a novel model, Conditional Multifactorial Contingency (CMC), that models the intertwined heterogeneities in all dimensions of the data tensor and infers the probability distribution of each entry of the data tensor jointly conditioned on these heterogeneities. The eventual and most comprehensive version of CMC can work on multidimensional binary or integer data tensors, even in cases where some values in the tensor are missing. CMC was initiated from elegant and simple statistical principles, derived through rigorous theoretical proofs, but ended up as a powerful tool being widely applicable to real-world biology/bioinformatics studies. Being empowered by CMC, we conducted four case studies for real-world bioinformatics research problems: (1) driving transcription factor (TF) identification; (2) scRNA-seq data normalization; (3) cancer-associated gene identification; (4) cell similarity quantification. For each of these case studies, we proposed a whole analysis framework and specific adaptation design for CMC. In each of them, our method based on CMC outperformed existing methods and provided inspiring clues for biological discoveries, which have been validated by biological experiments.

Yes / Inpatient falls are the most common safety incident reported by hospitals worldwide. Traditionally, responses have been guided by categorising patients' levels of fall risk, but multifactorial approaches are now recommended. These target individual, modifiable fall risk factors, requiring clear communication between multidisciplinary team members. Spoken communication is an important channel, but little is known about its form in this context. We aim to address this by exploring spoken communication between hospital staff about fall prevention and how this supports multifactorial fall prevention practice. Data were collected through semistructured qualitative interviews with 50 staff and ethnographic observations of fall prevention practices (251.25 hours) on orthopaedic and older person wards in four English hospitals. Findings were analysed using a framework approach. We observed staff engaging in 'multifactorial talk' to address patients' modifiable risk factors, especially during multidisciplinary meetings which were patient focused rather than risk type focused. Such communication coexisted with 'categorisation talk', which focused on patients' levels of fall risk and allocating nursing supervision to 'high risk' patients. Staff negotiated tensions between these different approaches through frequent 'hybrid talk', where, as well as categorising risks, they also discussed how to modify them. To support hospitals in implementing multifactorial, multidisciplinary fall prevention, we recommend: (1) focusing on patients' individual risk factors and actions to address them (a 'why?' rather than a 'who' approach); (2) where not possible to avoid 'high risk' categorisations, employing 'hybrid' communication which emphasises actions to modify individual risk factors, as well as risk level; (3) challenging assumptions about generic interventions to identify what individual patients need; and (4) timing meetings to enable staff from different disciplines to participate. / This research was funded by the National Institute for Health and Care Research Health Services and Delivery Research Programme (project number HSDR NIHR129488).

Delirium is probably the most common presenting symptom of disease in old age. Delirium, as defined in DSM-IV, is a neuropsychiatric syndrome characterized by disturbance in attention and consciousness, which develops over a short period of time and where the symptoms tend to fluctuate during the course of the day. The overall aim was to increase knowledge about the risk factors and outcome of delirium in old patients with femoral neck fracture and to develop and evaluate a multi-factorial intervention program for prevention and treatment of delirium in these patients. In a prospective study of 101 consecutive patients with a femoral neck fracture, 29.7% were delirious before surgery and another 18.8% developed delirium postoperatively. Of those who were delirious preoperatively all but one remained delirious postoperatively. The majority of those delirious before surgery were demented, treated with drugs with anticholinergic properties (mainly neuroleptics), had had previous episodes of delirium and had fallen indoors. Patients who developed postoperative delirium had perioperative falls in blood pressure and seemed to have more postoperative complications, such as infections. Patients with preoperative delirium had a poorer walking ability on discharge compared to patients with postoperative delirium only. In a five-year prospective follow up study 30 out of 78 (38.5%) non-demented patients with a femoral neck fracture developed dementia. Twenty out of 29 (69%) who were delirious postoperatively developed dementia compared to 10 out of 49 (20%) who were not delirious during hospitalization (p&lt;0.001). Twenty-one (72.4%) of those with postoperative delirium died within 5 years compared to 17/49 (34.7%) of those who remained lucid postoperatively (p=0.001). A non-randomized multi-factorial intervention study with the aim of preventing and treating delirium among patients with femoral neck fracture (n=49) showed that the incidence of delirium was significantly lower than reported in previously published studies. The incidence of other postoperative complications was also lower and a larger proportion of the patients regained independent walking ability and could return to their previous living conditions on discharge. A similar multi-factorial intervention program evaluated as a randomized controlled trial including 199 femoral neck fracture patients showed that fewer intervention patients than controls suffered postoperative delirium (56/102, 55% vs. 73/97, 75%, p=0.003). For intervention patients the postoperative delirium was also of shorter duration (5.0±7.1 days vs. 10.2±13.3 days, p=0.009). Eighteen percent in the intervention ward and 52% of controls were delirious after the seventh postoperative day (p&lt;0.001). Intervention patients suffered from significantly fewer in-hospital complications, such as decubital ulcers, urinary tract infections, nutritional complications, sleeping problems and falls, than controls. Total postoperative hospitalization was shorter in the intervention ward (28.0±17.9 days vs. 38.0±40.6 days, p=0.028). In conclusion, pre- and postoperative delirium is common and seems to be associated with various risk factors, which require different strategies for prevention and treatment. Delirium is also associated with the development of dementia and a higher mortality rate. Multifactorial intervention programs can successfully be implemented and result in the reduction of delirium, fewer complications and shorter hospitalization.

Nationally vehicle theft is associated with approximately 40 fatalities per year with an estimated annual cost of one billion dollars. During 2000 - 2001 almost 139,000 motor vehicles (cars, motor cycles, campervans, and trucks) were stolen across Australia. Vehicle theft is an overwhelmingly adolescent male crime yet gender has not been considered in either policy or program initiatives.----- This thesis used Spence's Multifactorial Gender Identity theory to examine the relationships between vehicle theft, offending, and adolescent male gender identity. Four central research questions were posed:----- 1. Is vehicle theft a gendered behaviour, that is, do some adolescent males engage in vehicle theft to create a particular adolescent male gender identity?----- 2. Do vehicle theft offenders engage in other offending behaviours?----- 3. Are these other offences also used to create a particular adolescent male gender identity and----- 4. Will the use of a variety of gender-related scales to measure gender identity support Spence's Multifactorial Gender Identity Theory that gender identity is multifactorial?----- Study One Parts A and B provided the empirical basis for Studies Two and Three. Part A of Study One examined the &quotmaleness" of vehicle theft and two other problem behaviours: problem drinking and traffic offence involvement. Cross-sectional and longitudinal methodologies were used to investigate a representative sample of 4,529 male high school students in relation to vehicle theft, problem drinking, and traffic offence involvement as a novice driver. Results indicated that &quotmaleness" was significantly related to vehicle theft, problem drinking, and traffic offence involvement. Subsequent analyses, based on Jessor's Problem Behaviour Theory, found a significant relationship between vehicle theft offenders and problem drinking. Study One Part B examined the relationship between masculinity as measured by the Australian Sex Role Scale (ASRS) and problem drinking in a rural sample of 1,248 male high school students. Using a cross sectional methodology, Masculine students were more likely than students in the other gender trait groups to report a range of problem drinking behaviours. Contrary to previous research, both socially desirable and socially undesirable masculine traits were significantly related to most problem drinking behaviours.----- Having established significant relationships between &quotmaleness" and vehicle theft and masculinity and the adolescent problem behaviour of underage drinking, Study Two qualitatively examined the perceptions of adolescent males with histories of vehicle theft in relation to &quotdoing masculinity". Using semi-structured interviews, 30 adolescent males, clients of the juvenile justice system were asked &quotwhat do you have to do to be a man?" Vehicle theft was clearly identified as a masculine defining behaviour as were other offending behaviours. Overall, participants nominated very traditional behaviours such as having a job and providing financially for families as essential behaviours in &quotdoing masculinity". It was suggested that in the absence of legal options for creating a masculine gender identity, some adolescent males adopted more readily accessed illegal options. Study Two also canvassed the driving behaviour of adolescent males in stolen vehicles. Crash involvement was not uncommon. Speed, alcohol, and the presence of other adolescent males were consistent characteristics of their driving behaviour. Indigenous and non-Indigenous participants were similar in their responses.----- Study Three compared the gender identity of offender and non-offender adolescent males as measured by three gender-related measures: the ASRS, the Toughness Subscale of the Male Role Norm Scale (TSMRNS) and the Doing Masculinity Composite Scale (DMCS). While the ASRS measured gender traits, the TSMRNS measured masculinity ideology. The DMCS was developed from the responses of participants in Study Two and sought to measure how participants &quotdo masculinity". Analyses indicated vehicle theft was endorsed by just over a third of the sample as a masculine defining behaviour. Overall, offenders were again very traditional in the behaviours they endorsed. When compared to non-offenders, offenders were more likely to endorse illegal behaviours in &quotdoing masculinity" while non-offenders were more likely to endorse legal behaviours. Both offenders and non-offenders strongly endorsed having a car and the ability to drive as masculine defining behaviours.----- In relation to gender traits, non-offenders were more likely than offenders to be classified as Masculine by the ASRS. Surprisingly offenders were more likely to be classified as Androgynous. In relation to masculinity ideology, offenders and non-offenders were similar in their results on the TSMRNS however offenders were more likely to endorse beliefs concerning the need to be tough. Overall Indigenous and non-Indigenous offenders were similar in their responses though Indigenous males were more likely to endorse beliefs concerning the need to be tough. Spence's Multifactorial Gender Identity theory was supported in that the relations between the three gender-related measures were significant but low.----- Results confirmed that vehicle theft was endorsed by a minority of participants as a gendered behaviour. Other offending behaviours were also endorsed by some adolescent males as means to create masculine gender identity. Importantly though both offenders and non-offenders endorsed very traditional behaviours in relation to &quotdoing masculinity". The implications for policy and program initiatives include the acknowledgement of gender identity as an important component in relation to vehicle theft and offending and the desire of adolescent male offenders to engage in legal, traditional male behaviours. In the absence of legal avenues however, some adolescent males may use illegal behaviours to create gender identity. Cars and driving also feature as important components of gender identity for both offenders and non-offenders and these needs to be considered in relation to road safety initiatives.