A Bayesian two-stage spatially dependent variable selection model for space–time health data

2018 ◽  
Vol 28 (9) ◽  
pp. 2570-2582 ◽  
Author(s):  
Jungsoon Choi ◽  
Andrew B Lawson
Keyword(s):  
Risk Factors ◽  
Variable Selection ◽  
Health Outcomes ◽  
Stage Structure ◽  
Health Data ◽  
Space Time ◽  
Regression Coefficients ◽  
Two Stage ◽  
Epidemiological Modeling ◽  
Confounding Bias

In space–time epidemiological modeling, most studies have considered the overall variations in relative risk to better estimate the effects of risk factors on health outcomes. However, the associations between risk factors and health outcomes may vary across space and time. Especially, the temporal patterns of the covariate effects may depend on space. Thus, we propose a Bayesian two-stage spatially dependent variable selection approach for space–time health data to determine the spatially varying subsets of regression coefficients with common temporal dependence. The two-stage structure allows reduction of the spatial confounding bias in the estimates of the regression coefficients. A simulation study is conducted to examine the performance of the proposed two-stage model. We apply the proposed model to the number of inpatients with lung cancer in 159 counties of Georgia, USA.

scholarly journals The Rapid Mood Screener: A Novel and Pragmatic Screener Tool for Bipolar I Disorder

CNS Spectrums ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 167-168
Author(s):  
C. Brendan Montano ◽  
Mehul Patel ◽  
Rakesh Jain ◽  
Prakash S. Masand ◽  
Amanda Harrington ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Health Outcomes ◽  
Screening Tool ◽  
Clinical Interview ◽  
Clinical Practices ◽  
Predictive Values ◽  
Bipolar I Disorder ◽  
Manic Symptoms ◽  
Depressive Episodes

AbstractIntroductionApproximately 70% of patients with bipolar disorder (BPD) are initially misdiagnosed, resulting in significantly delayed diagnosis of 7–10 years on average. Misdiagnosis and diagnostic delay adversely affect health outcomes and lead to the use of inappropriate treatments. As depressive episodes and symptoms are the predominant symptom presentation in BPD, misdiagnosis as major depressive disorder (MDD) is common. Self-rated screening instruments for BPD exist but their length and reliance on past manic symptoms are barriers to implementation, especially in primary care settings where many of these patients initially present. We developed a brief, pragmatic bipolar I disorder (BPD-I) screening tool that not only screens for manic symptoms but also includes risk factors for BPD-I (eg, age of depression onset) to help clinicians reduce the misdiagnosis of BPD-I as MDD.MethodsExisting questionnaires and risk factors were identified through a targeted literature search; a multidisciplinary panel of experts participated in 2 modified Delphi panels to select concepts thought to differentiate BPD-I from MDD. Individuals with self-reported BPD-I or MDD participated in cognitive debriefing interviews (N=12) to test and refine item wording. A multisite, cross-sectional, observational study was conducted to evaluate the screening tool’s predictive validity. Participants with clinical interview-confirmed diagnoses of BPD-I or MDD completed a draft 10-item screening tool and additional questionnaires/questions. Different combinations of item sets with various item permutations (eg, number of depressive episodes, age of onset) were simultaneously tested. The final combination of items and thresholds was selected based on multiple considerations including clinical validity, optimization of sensitivity and specificity, and pragmatism.ResultsA total of 160 clinical interviews were conducted; 139 patients had clinical interview-confirmed BPD-I (n=67) or MDD (n=72). The screening tool was reduced from 10 to 6 items based on item-level analysis. When 4 items or more were endorsed (yes) in this analysis sample, the sensitivity of this tool for identifying patients with BPD-I was 0.88 and specificity was 0.80; positive and negative predictive values were 0.80 and 0.88, respectively. These properties represent an improvement over the Mood Disorder Questionnaire, while using >50% fewer items.ConclusionThis new 6-item BPD-I screening tool serves to differentiate BPD-I from MDD in patients with depressive symptoms. Use of this tool can provide real-world guidance to primary care practitioners on whether more comprehensive assessment for BPD-I is warranted. Use of a brief and valid tool provides an opportunity to reduce misdiagnosis, improve treatment selection, and enhance health outcomes in busy clinical practices.FundingAbbVie Inc.

scholarly journals Availability of ultra-processed food associated with health outcomes: a systematic review

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
T Pereira de Araújo ◽  
M Moraes ◽  
V Magalhães ◽  
C Afonso ◽  
S Rodrigues
Keyword(s):  
Risk Factors ◽  
Health Outcomes ◽  
Food Consumption ◽  
Food Availability ◽  
Overweight And Obesity ◽  
Noncommunicable Diseases ◽  
Research Association ◽  
Processed Food ◽  
Household Budget ◽  
Household Budget Surveys

Abstract Background Ultra-processed food (UPF) consumption increases worldwide, which can be harm to population's health. To establish associations between UPF and health outcomes, food consumption can be assessed individually or by using availability data, such as purchase lists or household budget surveys. The aim of this review was to search for studies on the availability of UPF related with mortality and morbidity from noncommunicable diseases or their risk factors. Methods PRISMA guideline was used. Searches were performed on PubMED, EBSCO, Scopus and Web of Science on December 2019. Search strategy included terms related with exposure (UPF) and outcomes (mortality or morbidity from noncommunicable diseases and their risk factors). Studies were selected based on the title and abstracts. Full texts were screened for eligibility and the snowballing method was used to find other relevant studies. To be based on UPF availability data and its relation with health outcomes were the inclusion criteria. Studies that assessed only food consumption at an individual level and did not present health outcome were excluded. Selection was conducted by two reviewers and a third helped when disagreement occurred. Results After duplicates removal, 560 records were analyzed. From the 11 eligible studies, 55% were conducted in more than one country. Others were performed in Brazil (27%), Guatemala (9%) and Sweden (9%). Studies were ecological (64%), cross-sectional (27%) and longitudinal (9%). All had representative samples, 45.5% were national samples, and the others were from particular population subgroups. In all studies, the only health outcomes that showed positive association with UPF availability were overweight and obesity prevalence. Conclusions Studies relating ultra-processed food availability and health outcomes are mainly focused on overweight and obesity. It is thus necessary to further explore the relationship between UPF availability and other health outcomes. Key messages It is necessary to further research association between ultra-processed food availability and other health outcomes, such as morbidity and mortality from cardiovascular diseases, diabetes or cancer. Purchase lists or household budget surveys are an important source of food availability data and can be used to relate the consumption of ultra-processed foods to health outcomes.

scholarly journals Choice of a suitable diabetes risk assessment tool in Nepal – can we learn from Canada?

2016 ◽  
Vol 6 (2) ◽  
pp. 548-550
Author(s):  
Gina Agarwal ◽  
Brijesh Sathian ◽  
Sutapa Agrawal
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Health Outcomes ◽  
Assessment Tool ◽  
National Policy ◽  
Diabetes Risk ◽  
Risk Assessment Tool ◽  
Educational Tool ◽  
Diabetes Risk Factors ◽  
The Masses

If the population can be made more aware about diabetes by the use of a risk assessment tool as an educational tool as well, it could help to curb the diabetes epidemic in Nepal. Education of the masses about diabetes risk factors, prevention, and complications is urgently needed, using clear and simple messages. National policy efforts can be strengthened and health  outcomes improved when awareness is increased. Perhaps learning from Canada is a start, and Nepal will be able to make progress with something simple like ‘NEPAL-RISK’?

Space–Time Gradient Method for Unsteady Bladerow Interaction—Part II: Further Validation, Clocking, and Multidisturbance Effect

2015 ◽  
Vol 137 (12) ◽  
Author(s):  
L. He ◽  
J. Yi ◽  
P. Adami ◽  
L. Capone
Keyword(s):  
Case Studies ◽  
Flow Analysis ◽  
Space Time ◽  
Inlet Temperature ◽  
Turbine Stage ◽  
Two Stage ◽  
Transonic Compressor ◽  
Surface Time ◽  
Blade Row ◽  
Speed Up

For efficient and accurate unsteady flow analysis of blade row interactions, a space–time gradient (STG) method has been proposed. The development is aimed at maintaining as many modeling fidelities (the interface treatment in particular) of a direct unsteady time-domain method as possible while still having a significant speed-up. The basic modeling considerations, main method ingredients and some preliminary verification have been presented in Part I of the paper. Here in Part II, further case studies are presented to examine the capability and applicability of the method. Having tested a turbine stage in Part I, here we first consider the applicability and robustness of the method for a three-dimensional (3D) transonic compressor stage under a highly loaded condition with separating boundary layers. The results of the STG solution compare well with the direct unsteady solution while showing a speed up of 25 times. The method is also used to analyze rotor–rotor/stator–stator interferences in a two-stage turbine configuration. Remarkably, for stator–stator and rotor–rotor clocking analyses, the STG method demonstrates a significant further speed-up. Also interestingly, the two-stage case studies suggest clearly measurable clocking dependence of blade surface time-mean temperatures for both stator–stator clocking and rotor–rotor clocking, though only small efficiency variations are shown. Also validated and illustrated is the capacity of the STG method to efficiently evaluate unsteady blade forcing due to the rotor–rotor clocking. Considerable efforts are directed to extending the method to more complex situations with multiple disturbances. Several techniques are adopted to decouple the disturbances in the temporal terms. The developed capabilities have been examined for turbine stage configurations with inlet temperature distortions (hot streaks), and for three blade-row turbine configurations with nonequal blade counts. The results compare well with the corresponding direct unsteady solutions.

scholarly journals Oral health inequality in people with severe mental illness: a cross-sectional study using National Health and Nutrition Examination Survey 1999-2016

2021 ◽  
Author(s):  
Jing Kang ◽  
Jianhua Wu ◽  
Vishal Aggarwal ◽  
David Shiers ◽  
Tim Doran ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mental Illness ◽  
Oral Health ◽  
General Population ◽  
Health Outcomes ◽  
Severe Mental Illness ◽  
Tooth Loss ◽  
List Type ◽  
Cross Sectional ◽  
Oral Health Outcomes

AbstractOBJECTIVETo explore whether people with severe mental illness (SMI) experience worse oral health compared to the general population, and the risk factors for poor oral health in people with SMI.METHODThis study used cross-sectional data from the National Health and Nutrition Examination Survey (1999-2016) including on self-rated oral health, ache in mouth, tooth loss, periodontitis stage, and number of decayed, missing, and filled teeth. Candidate risk factors for poor oral health included demographic characteristics, lifestyle factors, physical health comorbidities, and dental hygiene behaviours. The authors used ordinal logistic regression and zero-inflated negative binomial models to explore predictors of oral health outcomes.RESULTS53,348 cases were included in the analysis, including 718 people with SMI. In the fully adjusted model, people with SMI were more likely to suffer from tooth loss (OR 1.40, 95% CI: 1.12-1.75). In people with SMI, the risk factors identified for poor oral health outcomes were older age, white ethnicity, lower income, smoking history, and diabetes. Engaging in physical activity and daily use of dental floss were associated with better oral health outcomes.CONCLUSIONSPeople with SMI experience higher rates of tooth loss than the general population, and certain subgroups are particularly at risk. Having a healthy lifestyle such as performing regular physical exercise and flossing may lower the risk of poor oral health. These findings suggest opportunities for targeted prevention and early intervention strategies to mitigate adverse oral health outcomes.Significant outcomes (x3)People with severe mental illness were at 40% higher risk of tooth loss when compared to the general population.Older adults, smokers and people with diabetes were at particularly high risk of poor oral health.Physical exercise and daily use of dental floss were associated with better oral health outcomes.Limitations (x3)The number of cases with data on periodontal disease was limited.The study was cross-sectional so causation could not be inferred.The analysis used prescriptions of antipsychotic and mood stabilising medication as a proxy measure of severe mental illness, as clinical diagnoses were not available in the dataset.Data availability statementThe NHANES 1999-2016 data is available at CDC website: https://www.cdc.gov/nchs/nhanes/index.htm, and is accessible and free to download for everyone.

scholarly journals Bayesian small-area modelling of COVID-19 cases and deaths in England and association with key risk factors: a combined space–time Susceptible-Exposed-Infected-Removed (SEIR) model

2020 ◽  
Author(s):  
Benn Sartorius ◽  
Andrew Lawson ◽  
Rachel L. Pullan
Keyword(s):  
Risk Factors ◽  
Small Area ◽  
Mortality Rates ◽  
Space Time ◽  
Spatiotemporal Variability ◽  
Population Mobility ◽  
Seir Model ◽  
Social Distancing ◽  
The Impact ◽  
Second Wave

Abstract Background: COVID-19 caseloads in England appear have passed through a first peak, with evidence of an emerging second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths, identify localised areas in space-time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at small-area resolution in coming weeks.Methods: We applied a Bayesian space–time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England (Middle Layer Super Output Area [MSOA], 6791 units) and by week (using observed data from week 5 to 34), including key determinants, the modelled transmission dynamics and spatial-temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA.Results: Reductions in population mobility due the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent steady increase signalling the start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates.Conclusions: While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have contributed to the current increase signalling the start of the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced.

scholarly journals Experiencia comparada europea y el reto de la descentralización sanitaria

2020 ◽  
Vol 32 (2) ◽  
pp. 841
Author(s):  
Carla Blázquez-Fernández ◽  
David Cantarero-Prieto ◽  
Marta Pascual-Sáez
Keyword(s):  
Health Care ◽  
Panel Data ◽  
Health Outcomes ◽  
Health Expenditure ◽  
Econometric Model ◽  
Fiscal Decentralization ◽  
Health Data ◽  
Panel Data Models ◽  
Demand And Supply ◽  
European Health

This paper analyzes the main characteristics of European health care decentralization models with special attention to the determinants of health outcomes and expenditures and proposes using panel data models, and data from OECD Health Data, an econometric model explaining their behaviour and evolution. The results show that income is the most important factor in explaining the volume of health expenditure both statically and dynamically, while other factors of demand and supply and the degree of decentralization or type of health system, despite also influence are less important. Instead, in health outcomes fiscal decentralization has a more mixed against other factors.

Merits and caveats of propensity scores to adjust for confounding

2018 ◽  
Vol 34 (10) ◽  
pp. 1629-1635 ◽  
Author(s):  
Edouard L Fu ◽  
Rolf H H Groenwold ◽  
Carmine Zoccali ◽  
Kitty J Jager ◽  
Merel van Diepen ◽  
...  
Keyword(s):  
Risk Factors ◽  
The Netherlands ◽  
Health Outcomes ◽  
Observational Data ◽  
Propensity Scores ◽  
Cooperative Study ◽  
Dialysis Patients ◽  
Proper Understanding ◽  
The Past ◽  
Using Data

Abstract Proper adjustment for confounding is essential when estimating the effects of treatments or risk factors on health outcomes in observational data. To this end, various statistical methods have been developed. In the past couple of years, the use of propensity scores (PSs) to control for confounding has increased. Proper understanding of this method is necessary to critically appraise research in which it is applied. In this article, we provide an overview of PS methods, explaining their concept, advantages and possible disadvantages. Furthermore, the use of PS matching, PS adjustment and PS weighting is illustrated using data from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) cohort of dialysis patients.

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