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Clark AL, Thomas KR, Ortega N, Haley AP, Duarte A, O'Bryant S. Empirically derived psychosocial-behavioral phenotypes in Black/African American and Hispanic/Latino older adults enrolled in HABS-HD: Associations with AD biomarkers and cognitive outcomes. Alzheimers Dement 2024; 20:1360-1373. [PMID: 37990803 PMCID: PMC10917046 DOI: 10.1002/alz.13544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/21/2023] [Accepted: 10/14/2023] [Indexed: 11/23/2023]
Abstract
INTRODUCTION Identification of psychosocial-behavioral phenotypes to understand within-group heterogeneity in risk and resiliency to Alzheimer's disease (AD) within Black/African American and Hispanic/Latino older adults is essential for the implementation of precision health approaches. METHODS A cluster analysis was performed on baseline measures of socioeconomic resources (annual income, social support, occupational complexity) and psychiatric distress (chronic stress, depression, anxiety) for 1220 racially/ethnically minoritized adults enrolled in the Health and Aging Brain Study-Health Disparities (HABS-HD). Analyses of covariance adjusting for sociodemographic factors examined phenotype differences in cognition and plasma AD biomarkers. RESULTS The cluster analysis identified (1) Low Resource/High Distress (n = 256); (2) High Resource/Low Distress (n = 485); and (3) Low Resource/Low Distress (n = 479) phenotypes. The Low Resource/High Distress phenotype displayed poorer cognition and higher plasma neurofilament light chain; differences between the High Resource/Low Distress and Low Resource/Low Distress phenotypes were minimal. DISCUSSION The identification of psychosocial-behavioral phenotypes within racially/ethnically minoritized older adults is crucial to the development of targeted AD prevention and intervention efforts.
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Affiliation(s)
- Alexandra L. Clark
- Department of PsychologyThe University of Texas at AustinAustinTexasUSA
- Research ServiceVA San Diego Healthcare SystemSan DiegoCaliforniaUSA
| | - Kelsey R. Thomas
- Research ServiceVA San Diego Healthcare SystemSan DiegoCaliforniaUSA
- Department of PsychiatryUniversity of California San Diego Medical SchoolLa JollaCaliforniaUSA
| | - Nazareth Ortega
- Department of PsychologyThe University of Texas at AustinAustinTexasUSA
| | - Andreana P. Haley
- Department of PsychologyThe University of Texas at AustinAustinTexasUSA
| | - Audrey Duarte
- Department of PsychologyThe University of Texas at AustinAustinTexasUSA
| | - Sid O'Bryant
- Institute for Translational ResearchUniversity of North Texas Health Science CenterFort WorthTexasUSA
- Department of Family MedicineUniversity of North Texas Health Science CenterFort WorthTexasUSA
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Muschalla B. Are retired persons fitter in their psychological capacities than unemployed? A cross-sectional representative study in Germany. BMJ Open 2024; 14:e065869. [PMID: 38238046 PMCID: PMC10806760 DOI: 10.1136/bmjopen-2022-065869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 12/21/2023] [Indexed: 01/23/2024] Open
Abstract
OBJECTIVES Beyond specific aspects of numerical or verbal intelligence or cognitive speed, a broad range of psychological capacities are generally important in school, job and social life for all age groups. People have to quit the labour market up from a certain age about 65, whereas (younger) unemployed are motivated for return to work. The question is which psychological capacity profiles can be found in different employment groups (employed, mini-jobbers, voluntary service, retired, unemployed). DESIGN A representative cross-sectional survey was conducted in Germany, reaching 2528 persons. SETTING Republic of Germany. PARTICIPANTS Randomly selected inhabitants throughout Germany. PRIMARY AND SECONDARY OUTCOME MEASURES Participants reported their sociodemographic and work characteristics, as well as their psychological capacity profiles (Mini-ICF-APP-S) and work-related specific mental health problems (work-anxiety, embitterment). RESULTS The unemployed had-compared with all other groups-highest rates of work-anxiety and embitterment (16.3%). In contrast to the unemployed, the 'older' (70 aged) retired group, who were no longer working on the labour market, seldomly reported work-anxiety (2.6%) or embitterment (4.2%). The unemployed had the worst capacity profiles, most frequently no school degree (11.5%), most unemployment in their history (four times, as compared with once in the older retired). The psychological capacity profiles of the retired were similar to employed persons. CONCLUSIONS Keeping older persons with high psychological capacity levels in working life could be an alternative to forced reintegration of people with chronic participation problems into the competitive labour market. Unemployed persons with chronic health and participation problems might benefit from other social inclusion means.
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Soloveva MV, Poudel G, Barnett A, Shaw JE, Martino E, Knibbs LD, Anstey KJ, Cerin E. Characteristics of urban neighbourhood environments and cognitive age in mid-age and older adults. Health Place 2023; 83:103077. [PMID: 37451077 DOI: 10.1016/j.healthplace.2023.103077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/29/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023]
Abstract
In this cross-sectional study, we examined the extent to which features of the neighbourhood natural, built, and socio-economic environments were related to cognitive age in adults (N = 3418, Mage = 61 years) in Australia. Machine learning estimated an individual's cognitive age from assessments of processing speed, verbal memory, premorbid intelligence. A 'cognitive age gap' was calculated by subtracting chronological age from predicted cognitive age and was used as a marker of cognitive age. Greater parkland availability and higher neighbourhood socio-economic status were associated with a lower cognitive age gap score in confounder- and mediator-adjusted regression models. Cross-sectional design is a limitation. Living in affluent neighbourhoods with access to parks maybe beneficial for cognitive health, although selection mechanisms may contribute to the findings.
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Affiliation(s)
- Maria V Soloveva
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, 3000, Australia.
| | - Govinda Poudel
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, 3000, Australia
| | - Anthony Barnett
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, 3000, Australia
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia; School of Life Sciences, La Trobe University, Melbourne, VIC, 3086, Australia
| | - Erika Martino
- School of Population and Global Health, University of Melbourne, Melbourne, VIC, 3053, Australia
| | - Luke D Knibbs
- School of Public Health, The University of Sydney, NSW 2006, Australia; Public Health Research Analytics and Methods for Evidence, Public Health Unit, Sydney Local Health District, Camperdown, NSW 2050, Australia
| | - Kaarin J Anstey
- School of Psychology, University of New South Wales, Kensington, NSW, 2052, Australia; Neuroscience Research Australia (NeuRA), Sydney, NSW, 2031, Australia; UNSW Ageing Futures Institute, Kensington, NSW, 2052, Australia
| | - Ester Cerin
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, 3000, Australia; School of Public Health, The University of Hong Kong, Hong Kong, China; Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia; Department of Community Medicine, UiT the Artic University of Norway, 9019, Tromsø, Norway
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Albagmi FM, Hussain M, Kamal K, Sheikh MF, AlNujaidi HY, Bah S, Althumiri NA, BinDhim NF. Predicting Multimorbidity Using Saudi Health Indicators (Sharik) Nationwide Data: Statistical and Machine Learning Approach. Healthcare (Basel) 2023; 11:2176. [PMID: 37570417 PMCID: PMC10418949 DOI: 10.3390/healthcare11152176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/12/2023] [Accepted: 07/20/2023] [Indexed: 08/13/2023] Open
Abstract
The Saudi population is at high risk of multimorbidity. The risk of these morbidities can be reduced by identifying common modifiable behavioural risk factors. This study uses statistical and machine learning methods to predict factors for multimorbidity in the Saudi population. Data from 23,098 Saudi residents were extracted from the "Sharik" Health Indicators Surveillance System 2021. Participants were asked about their demographics and health indicators. Binary logistic models were used to determine predictors of multimorbidity. A backpropagation neural network model was further run using the predictors from the logistic regression model. Accuracy measures were checked using training, validation, and testing data. Females and smokers had the highest likelihood of experiencing multimorbidity. Age and fruit consumption also played a significant role in predicting multimorbidity. Regarding model accuracy, both logistic regression and backpropagation algorithms yielded comparable outcomes. The backpropagation method (accuracy 80.7%) was more accurate than the logistic regression model (77%). Machine learning algorithms can be used to predict multimorbidity among adults, particularly in the Middle East region. Different testing methods later validated the common predicting factors identified in this study. These factors are helpful and can be translated by policymakers to consider improvements in the public health domain.
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Affiliation(s)
- Faisal Mashel Albagmi
- College of Applied Medical Sciences, Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia;
| | - Mehwish Hussain
- College of Public Health, Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia; (H.Y.A.); (S.B.)
| | - Khurram Kamal
- Department of Engineering Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan;
| | - Muhammad Fahad Sheikh
- Department of Mechanical Engineering, University of Management and Technology, Sialkot Campus, Lahore 54770, Pakistan;
| | - Heba Yaagoub AlNujaidi
- College of Public Health, Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia; (H.Y.A.); (S.B.)
| | - Sulaiman Bah
- College of Public Health, Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia; (H.Y.A.); (S.B.)
| | - Nora A. Althumiri
- Sharik Association for Research and Studies, Abubaker Alsedeq, Riyadh 13326, Saudi Arabia; (N.A.A.); (N.F.B.)
| | - Nasser F. BinDhim
- Sharik Association for Research and Studies, Abubaker Alsedeq, Riyadh 13326, Saudi Arabia; (N.A.A.); (N.F.B.)
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Qasrawi R, Vicuna Polo S, Abu Khader R, Abu Al-Halawa D, Hallaq S, Abu Halaweh N, Abdeen Z. Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments. Front Psychiatry 2023; 14:1071622. [PMID: 37304448 PMCID: PMC10250653 DOI: 10.3389/fpsyt.2023.1071622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction Mental health and cognitive development are critical aspects of a child's overall well-being; they can be particularly challenging for children living in politically violent environments. Children in conflict areas face a range of stressors, including exposure to violence, insecurity, and displacement, which can have a profound impact on their mental health and cognitive development. Methods This study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age. Results This study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age. Discussion The findings can inform evidence-based strategies for preventing and mitigating the detrimental effects of political violence on individuals and communities, highlighting the importance of addressing the needs of children in conflict-affected areas and the potential of using technology to improve their well-being.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Sciences, Al-Quds University, Jerusalem, Palestine
- Department of Computer Engineering, Istinye University, Istanbul, Türkiye
| | - Stephanny Vicuna Polo
- Al-Quds Center for Business Innovation and Entrepreneurship, Al-Quds University, Jerusalem, Palestine
| | - Rami Abu Khader
- Al-Quds Center for Business Innovation and Entrepreneurship, Al-Quds University, Jerusalem, Palestine
| | | | - Sameh Hallaq
- Al-Quds Bard College for Arts and Sciences, Al-Quds University, Jerusalem, Palestine
| | - Nael Abu Halaweh
- Department of Computer Sciences, Al-Quds University, Jerusalem, Palestine
| | - Ziad Abdeen
- Faculty of Medicine, Al-Quds University, Jerusalem, Palestine
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Qin Y, Wu J, Xiao W, Wang K, Huang A, Liu B, Yu J, Li C, Yu F, Ren Z. Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192215027. [PMID: 36429751 PMCID: PMC9690067 DOI: 10.3390/ijerph192215027] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/04/2022] [Accepted: 11/10/2022] [Indexed: 06/01/2023]
Abstract
The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning models for diabetes prediction using lifestyle data from the National Health and Nutrition Examination Survey (NHANES) database. The 1999-2020 NHANES database yielded data on 17,833 individuals data based on demographic characteristics and lifestyle-related variables. To screen training data for machine models, the Akaike Information Criterion (AIC) forward propagation algorithm was utilized. For predicting diabetes, five machine-learning models (CATBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM)) were developed. Model performance was evaluated using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic (ROC) curve. Among the five machine-learning models, the dietary intake levels of energy, carbohydrate, and fat, contributed the most to the prediction of diabetes patients. In terms of model performance, CATBoost ranks higher than RF, LG, XGBoost, and SVM. The best-performing machine-learning model among the five is CATBoost, which achieves an accuracy of 82.1% and an AUC of 0.83. Machine-learning models based on NHANES data can assist medical institutions in identifying diabetes patients.
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Affiliation(s)
- Yifan Qin
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Jinlong Wu
- College of Physical Education, Southwest University, Chongqing 400715, China
| | - Wen Xiao
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Kun Wang
- Physical Education College, Yanching Institute of Technology, Langfang 065201, China
| | - Anbing Huang
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Bowen Liu
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Jingxuan Yu
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Chuhao Li
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Fengyu Yu
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Zhanbing Ren
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
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