Identifying, Measuring, and Ranking Social Determinants of Health for Health Promotion Interventions Targeting Informal Settlement Residents.

IF 2.8 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Journal of Preventive Medicine and Public Health Pub Date : 2023-07-01 DOI:10.3961/jpmph.23.059
Farhad Nosrati Nejad, Mohammad Reza Ghamari, Seyed Hossein Mohaqeqi Kamal, Seyed Saeed Tabatabaee
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Abstract

Objectives: Considering the importance of social determinants of health (SDHs) in promoting the health of residents of informal settlements and their diversity, abundance, and breadth, this study aimed to identify, measure, and rank SDHs for health promotion interventions targeting informal settlement residents in a metropolitan area in Iran.

Methods: Using a hybrid method, this study was conducted in 3 phases from 2019 to 2020. SDHs were identified by reviewing studies and using the Delphi method. To examine the SDHs among informal settlement residents, a cross-sectional analysis was conducted using researcher-made questionnaires. Multilayer perceptron analysis using an artificial neural network was used to rank the SDHs by priority.

Results: Of the 96 determinants identified in the first phase of the study, 43 were examined, and 15 were identified as high-priority SDHs for use in health-promotion interventions for informal settlement residents in the study area. They included individual health literacy, nutrition, occupational factors, housing-related factors, and access to public resources.

Conclusions: Since identifying and addressing SDHs could improve health justice and mitigate the poor health status of settlement residents, ranking these determinants by priority using artificial intelligence will enable policymakers to improve the health of settlement residents through interventions targeting the most important SDHs.

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针对非正式定居居民的健康促进干预的健康社会决定因素的识别、测量和排名。
考虑到健康社会决定因素(sdh)在促进非正式住区居民健康方面的重要性及其多样性、丰富性和广度,本研究旨在确定、衡量和排名针对伊朗大都市地区非正式住区居民的健康促进干预措施的sdh。方法:采用混合方法,于2019 - 2020年分三期进行研究。通过文献回顾和德尔菲法对sdh进行鉴定。为了研究非正式住区居民的sdh,采用研究者自制的问卷进行了横断面分析。利用人工神经网络的多层感知器分析对sdh进行优先级排序。结果:在研究第一阶段确定的96个决定因素中,对43个进行了检查,其中15个被确定为高优先级sdh,用于研究地区非正式住区居民的健康促进干预措施。这些因素包括个人健康素养、营养、职业因素、住房相关因素以及获得公共资源的机会。结论:由于识别和解决sdh可以改善健康正义并缓解定居点居民的不良健康状况,因此使用人工智能对这些决定因素进行优先排序将使政策制定者能够通过针对最重要的sdh的干预措施来改善定居点居民的健康状况。
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来源期刊
Journal of Preventive Medicine and Public Health
Journal of Preventive Medicine and Public Health Medicine-Public Health, Environmental and Occupational Health
CiteScore
6.40
自引率
0.00%
发文量
60
审稿时长
8 weeks
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