scholarly journals The impact of social housing on mental health: longitudinal analyses using marginal structural models and machine learning-generated weights

2018 ◽  
Vol 47 (5) ◽  
pp. 1414-1422 ◽  
Author(s):  
Rebecca Bentley ◽  
Emma Baker ◽  
Koen Simons ◽  
Julie A Simpson ◽  
Tony Blakely
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Housing ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Longitudinal Analyses ◽  
The Impact

scholarly journals Exploring the relationship between housing concerns, mental health and wellbeing: a qualitative study of social housing tenants

2019 ◽  
Vol 42 (3) ◽  
pp. e231-e238 ◽  
Author(s):  
Eleanor Holding ◽  
Lindsay Blank ◽  
Mary Crowder ◽  
Edward Ferrari ◽  
Elizabeth Goyder
Keyword(s):  
Mental Health ◽  
Low Income ◽  
Social Housing ◽  
Mental Health Problems ◽  
Poor Mental Health ◽  
Housing Quality ◽  
Community Safety ◽  
Public Health Issue ◽  
Health And Wellbeing ◽  
The Impact

Abstract Background The rising prevalence of mental health problems is a growing public health issue. Poor mental health is not equally distributed across social groups and is associated with poverty and insecure housing. An evaluation of a social housing intervention provided an opportunity to explore the connections between housing and wider determinants of health and wellbeing. Methods We undertook 44 interviews with social housing tenants over a two-year period to explore their views on housing, health and wellbeing. Results Poor mental health was common. The results suggest that perceptions of housing quality, service responsiveness, community safety, benefit changes and low income all have a detrimental effect on tenants’ mental health. Conclusions Social housing providers who wish to have a positive impact on the mental health of their tenants need to consider how to best support or mitigate the impact of these stresses. Addressing traditional housing officer functions such as reporting or monitoring home repairs alongside holistic support remains an important area where social housing departments can have substantial health impact. Tackling the complex nature of mental health requires a joined up approach between housing and a number of services.

Estimating the Causal Effect of Some Exposure From Nonrandomized Studies: The Example of Reduced Intensity Conditioning (RIC) in Hematological Diseases.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3365-3365
Author(s):  
Matthieu Resche-Rigon ◽  
Marie Robin ◽  
Regis Peffault de Latour ◽  
Sylvie Chevret ◽  
Gerard P Socie
Keyword(s):  
Quantitative Methods ◽  
Causal Effect ◽  
Autologous Transplantation ◽  
Structural Models ◽  
Marginal Structural Models ◽  
Reduced Intensity Conditioning ◽  
Hematological Diseases ◽  
Causal Pathway ◽  
The Impact ◽  
Reduced Intensity

Abstract Abstract 3365 Poster Board III-253 Introduction: Although allogeneic SCT with RIC has now gained wide acceptance, its eventual benefit again non-transplant approach is largely unknown (outside the setting of large randomized trials). When evaluating the impact on survival of reduced intensity conditioning in malignant hematological diseases, standard estimations based on Cox regression from observational databases could be biased because they ignore covariates that confound treatment decision. In this setting, we applied and compared two different statistical methods that were developed to control for confounding in estimating exposure (or treatment) effect from epidemiological studies. Patients and Methods: The statistical challenge was that allograft tended to be given when a patient was in advanced phase of his/her hematological malignancy, so that treatment was confounded by performance indicators, which in turn lie on the causal pathway between treatment and outcome. Thus, comparison of outcome first used propensity score (PS) analyses that attempt to create a comparison group of non-treated patients that closely resembles the group of treated patients by matching for the likelihood that a given patient has received the treatment. Then, we used marginal structural models (MSMs) that consist in creating, by using inverse probability of treatment weights, a pseudo-population in which the probability of treatment does no longer depend on covariates, and the effect of treatment on outcome is the same as in the original population. Result: Reduced intensity conditioning allograft was performed in 82 patients with chemotherapy-sensitive patients relapsing after autologous transplantation. Patients with myeloma (MM, 23 pts), follicular lymphoma (FL, 28 pts) or Hodgkin disease (HD, 31 pts), were compared to 276 patients who relapsed after autologous transplantation but did not underwent allogeneic stem cell transplantation (142 MM, 115 FL and 19 HD). From original datasets, 21 (91%) matched pairs could be constituted from MM patients, as compared to 19 (68%) of the FL patients, down to 15 (48%) of the HD patients. Based on these PS-matched samples, a significant benefit of reduced intensity conditioning as compared with non allografted patients was observed in MM, with estimated hazard ratio (HR) of death at 0.34 (95% confidence interval, CI: 0.14-0.88), as well as in FL (HR= 0.78, 95%CI: 0.27;2.30) and in HD (HR= 0.24; 95%CI: 0.09-0.62). MSM-based analyses that applied to the reweighted populations confirmed these trends towards survival benefits in FL, though partially erased in MM and HD. Conclusions: We reported the application of marginal structural models, a new class of causal models to estimate the effect of nonrandomized treatments as an alternative to PS based approaches in small samples. We expect that an increasing number of physicians involved in clinical cohorts become familiar with these novel and appealing quantitative methods when assessing innovative treatment effects. Disclosures: No relevant conflicts of interest to declare.

scholarly journals The Impact of Sparse Follow-up on Marginal Structural Models for Time-to-Event Data

2015 ◽  
pp. kwv152
Author(s):  
Nassim Mojaverian ◽  
Erica E. M. Moodie ◽  
Alex Bliu ◽  
Marina B. Klein
Keyword(s):  
Structural Models ◽  
Marginal Structural Models ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
The Impact

scholarly journals The effect of delaying initiation with umeclidinium/vilanterol in patients with COPD: an observational administrative claims database analysis using marginal structural models

2018 ◽  
Vol 13 ◽  
Author(s):  
Beth Hahn ◽  
Ami R. Buikema ◽  
Lee Brekke ◽  
Amy Anderson ◽  
Eleena Koep ◽  
...  
Keyword(s):  
Structural Models ◽  
Medical Costs ◽  
Marginal Structural Models ◽  
Severe Exacerbation ◽  
Administrative Claims ◽  
Research Database ◽  
Adjusted Cost ◽  
Exacerbation Risk ◽  
The Impact

Background: Chronic obstructive pulmonary disease (COPD) is associated with high clinical and economic burden. Optimal pharmacological therapy for COPD aims to reduce symptoms and the frequency and severity of exacerbations. Umeclidinium/vilanterol (UMEC/VI) is an approved combination therapy for once-daily maintenance treatment of patients with COPD. This study evaluated the impact of delaying UMEC/VI initiation on medical costs and exacerbation risk. Methods: A retrospective analysis of patients with COPD who initiated UMEC/VI between 4/28/2014 and 7/31/2016 was conducted using the Optum Research Database. The index date was the first COPD visit after UMEC/VI available on US formulary (Commercial 4/28/2014; Medicare Advantage 1/1/2015). Patients were followed for 12 months post-index, and categorized into 12 cohorts corresponding to month (30-day period) of UMEC/VI initiation (i.e. Months 1–12) post-index. The outcomes studied during the follow up period included COPD-related and all-cause medical costs, and risk of COPD exacerbations. Marginal structural models (MSM) were used to control for time-varying confounding due to changes in treatment and severity during follow up. Results: 2,200 patients initiating UMEC/VI were included in the study sample. Patients’ average age was 69.3 years, 49.9% were female and 69.7% were Medicare insured. Following MSM analysis, 12-month adjusted COPD-related medical costs increased by 2.9% (95% confidence interval [CI]: 0.1–5.9%; p = 0.044) for each monthly delay in UMEC/VI initiation, with a 37.4% higher adjusted cost for patients initiating UMEC/VI in Month 12 versus Month 1 ($13,087 vs. $9524). The 12-month adjusted all-cause medical costs increased by 2.8% (95% CI: 0.6–5.2%; p = 0.013) for each monthly delay, with a 36.1% higher adjusted cost for patients initiating UMEC/VI at Month 12 versus Month 1 ($22,766 vs. $16,727). The monthly risk of severe exacerbation was significantly higher in patients who had not yet initiated UMEC/VI than those who had (hazard ratio: 1.74; 95% CI: 1.35–2.23; p < 0.001). Conclusions: Prompt use of UMEC/VI following a physician visit for COPD appears to result in economic and clinical benefits, with reductions in medical costs and exacerbation risk. Additional research is warranted to assess the benefits of initiating UMEC/VI as a first-line therapy compared with escalation to UMEC/VI from monotherapies.

scholarly journals A Machine Learning Technique to Analyze Depressive Disorders

2021 ◽  
Author(s):  
Dixita Mali ◽  
Kritika Kumawat ◽  
Gaurav Kumawat ◽  
Prasun Chakrabarti ◽  
Sandeep Poddar ◽  
...  
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Machine Learning ◽  
Depressive Disorders ◽  
Random Trees ◽  
Machine Learning Technique ◽  
Machine Learning Classifiers ◽  
Large Numbers ◽  
Learning Technique ◽  
The Impact

Abstract Depression is an ordinary mental health care problem and the usual cause of disability worldwide. The main purpose of this research was to determine that how depression affects the life of an individual. It is a leading cause of morbidity and death. Over the last 50–60 years, large numbers of studies published various aspects including the impact of depression. The main purpose of this research is to determine whether the person is suffering from depression or not. The dataset of Depression has been taken from the Kaggle website. Guided Machine Learning classifiers have helped in the highest accuracy of a dataset. Classifiers like XGBoost Tree, Random Trees, Neural Network, SVM, Random Forest, C5.0, and Bay Net. From the result, it is evident that the C5.0 classifier is giving the highest accuracy with 83.94 % and for each classifier, the result is derived based without pre-processing.

The cumulative effect of living with disability on mental health in working-age adults: an analysis using marginal structural models

2019 ◽  
Vol 55 (3) ◽  
pp. 309-318 ◽  
Author(s):  
Amalia Karahalios ◽  
Frank Pega ◽  
Zoe Aitken ◽  
Allison Milner ◽  
Julie A. Simpson ◽  
...  
Keyword(s):  
Mental Health ◽  
Structural Models ◽  
Cumulative Effect ◽  
Marginal Structural Models ◽  
Working Age

scholarly journals Improving Moderator Responsiveness in Online Peer Support Through Automated Triage (Preprint)

2018 ◽  
Author(s):  
David N Milne ◽  
Kathryn L. McCabe ◽  
Rafael A. Calvo
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Peer Support ◽  
Response Latency ◽  
Mental Health Problems ◽  
Well Being ◽  
Unique Contribution ◽  
Triage System ◽  
Percentage Points ◽  
The Impact

BACKGROUND Online peer support forums require oversight to ensure they remain safe and therapeutic. As online communities grow, they place a greater burden on their human moderators, which increases the likelihood that people at risk may be overlooked. This study evaluated the potential for machine learning to assist online peer support by directing moderators’ attention where it is most needed. OBJECTIVE This study aimed to evaluate the accuracy of an automated triage system and the extent to which it influences moderator behavior. METHODS A machine learning classifier was trained to prioritize forum messages as green, amber, red, or crisis depending on how urgently they require attention from a moderator. This was then launched as a set of widgets injected into a popular online peer support forum hosted by ReachOut.com, an Australian Web-based youth mental health service that aims to intervene early in the onset of mental health problems in young people. The accuracy of the system was evaluated using a holdout test set of manually prioritized messages. The impact on moderator behavior was measured as response ratio and response latency, that is, the proportion of messages that receive at least one reply from a moderator and how long it took for these replies to be made. These measures were compared across 3 periods: before launch, after an informal launch, and after a formal launch accompanied by training. RESULTS The algorithm achieved 84% f-measure in identifying content that required a moderator response. Between prelaunch and post-training periods, response ratios increased by 0.9, 4.4, and 10.5 percentage points for messages labelled as crisis, red, and green, respectively, but decreased by 5.0 percentage points for amber messages. Logistic regression indicated that the triage system was a significant contributor to response ratios for green, amber, and red messages, but not for crisis messages. Response latency was significantly reduced (P<.001), between the same periods, by factors of 80%, 80%, 77%, and 12% for crisis, red, amber, and green messages, respectively. Regression analysis indicated that the triage system made a significant and unique contribution to reducing the time taken to respond to green, amber, and red messages, but not to crisis messages, after accounting for moderator and community activity. CONCLUSIONS The triage system was generally accurate, and moderators were largely in agreement with how messages were prioritized. It had a modest effect on response ratios, primarily because moderators were already more likely to respond to high priority content before the introduction of triage. However, it significantly and substantially reduced the time taken for moderators to respond to prioritized content. Further evaluations are needed to assess the impact of mistakes made by the triage algorithm and how changes to moderator responsiveness impact the well-being of forum members.

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