Tong Lai Kun, Li Yue Yi, Liu Yong Bing, Zheng Mu Rui, Fu Guang Lei, Au Mio Leng
Research Management and Development Department, Kiang Wu Nursing College of Macau, Macao, China.
Education Department, Kiang Wu Nursing College of Macau, Macao, China.
EPMA J. 2024 May 10;15(2):221-232. doi: 10.1007/s13167-024-00365-5. eCollection 2024 Jun.
Suboptimal health is identified as a reversible phase occurring before chronic diseases manifest, emphasizing the significance of early detection and intervention in predictive, preventive, and personalized medicine (PPPM/3PM). While the biological and genetic factors associated with suboptimal health have received considerable attention, the influence of social determinants of health (SDH) remains relatively understudied. By comprehensively understanding the SDH influencing suboptimal health, healthcare providers can tailor interventions to address individual needs, improving health outcomes and facilitating the transition to optimal well-being. This study aimed to identify distinct profiles within SDH indicators and examine their association with suboptimal health status.
This cross-sectional study was conducted from June 16 to September 23, 2023, in five regions of China. Various SDH indicators, such as family health, economic status, eHealth literacy, mental disorder, social support, health behavior, and sleep quality, were examined in this study. Latent profile analysis was employed to identify distinct profiles based on these SDH indicators. Logistic regression analysis by profile was used to investigate the association between these profiles and suboptimal health status.
The analysis included 4918 individuals. Latent profile analysis revealed three distinct profiles (prevalence): the Adversely Burdened Vulnerability Group (37.6%), the Adversity-Driven Struggle Group (11.7%), and the Advantaged Resilience Group (50.7%). These profiles exhibited significant differences in suboptimal health status ( < 0.001). The Adversely Burdened Vulnerability Group had the highest risk of suboptimal health, followed by the Adversity-Driven Struggle Group, while the Advantaged Resilience Group had the lowest risk.
Distinct profiles based on SDH indicators are associated with suboptimal health status. Healthcare providers should integrate SDH assessment into routine clinical practice to customize interventions and address specific needs. This study reveals that the group with the highest risk of suboptimal health stands out as the youngest among all the groups, underscoring the critical importance of early intervention and targeted prevention strategies within the framework of 3PM. Tailored interventions for the Adversely Burdened Vulnerability Group should focus on economic opportunities, healthcare access, healthy food options, and social support. Leveraging their higher eHealth literacy and resourcefulness, interventions empower the Adversity-Driven Struggle Group. By addressing healthcare utilization, substance use, and social support, targeted interventions effectively reduce suboptimal health risks and improve well-being in vulnerable populations.
The online version contains supplementary material available at 10.1007/s13167-024-00365-5.
健康欠佳被视为慢性疾病显现之前的一个可逆阶段,这凸显了在预测性、预防性和个性化医学(PPPM/3PM)中早期检测和干预的重要性。虽然与健康欠佳相关的生物学和遗传因素已受到广泛关注,但健康的社会决定因素(SDH)的影响仍相对研究不足。通过全面了解影响健康欠佳的SDH,医疗保健提供者可以量身定制干预措施以满足个体需求,改善健康结果,并促进向最佳健康状态的转变。本研究旨在确定SDH指标中的不同类别,并检验它们与健康欠佳状态的关联。
本横断面研究于2023年6月16日至9月23日在中国的五个地区进行。本研究考察了各种SDH指标,如家庭健康、经济状况、电子健康素养、精神障碍、社会支持、健康行为和睡眠质量。采用潜在类别分析基于这些SDH指标确定不同类别。通过类别进行逻辑回归分析以研究这些类别与健康欠佳状态之间的关联。
分析纳入了4918名个体。潜在类别分析揭示了三种不同类别(患病率):负担沉重的弱势群体(37.6%)、逆境驱动的奋斗群体(11.7%)和优势适应力群体(50.7%)。这些类别在健康欠佳状态方面存在显著差异(<0.001)。负担沉重的弱势群体健康欠佳风险最高,其次是逆境驱动的奋斗群体,而优势适应力群体风险最低。
基于SDH指标的不同类别与健康欠佳状态相关。医疗保健提供者应将SDH评估纳入常规临床实践,以定制干预措施并满足特定需求。本研究表明,健康欠佳风险最高的群体在所有群体中是最年轻的,这突出了在3PM框架内早期干预和针对性预防策略的至关重要性。针对负担沉重的弱势群体的定制干预措施应侧重于经济机会、医疗保健可及性、健康食品选择和社会支持。利用逆境驱动的奋斗群体较高的电子健康素养和应变能力来实施干预措施。通过解决医疗保健利用、物质使用和社会支持问题,针对性干预措施可有效降低弱势群体健康欠佳风险并改善其健康状况。
在线版本包含可在10.1007/s13167-024-00365-5获取的补充材料。