Kapaon David, Riumallo-Herl Carlos, Jennings Elyse, Abrahams-Gessel Shafika, Makofane Keletso, Kabudula Chodziwadziwa Whiteson, Harling Guy
Harvard Center for Population and Development Studies, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.
PLOS Glob Public Health. 2024 Sep 9;4(9):e0003683. doi: 10.1371/journal.pgph.0003683. eCollection 2024.
The mechanisms connecting various types of social support to mortality have been well-studied in high-income countries. However, less is known about how these relationships function in different socioeconomic contexts. We examined how four domains of social support-emotional, physical, financial, and informational-impact mortality within a sample of older adults living in a rural and resource-constrained setting. Using baseline survey and longitudinal mortality data from HAALSI, we assessed how social support affects mortality in a cohort of 5059 individuals aged 40 years or older in rural Mpumalanga, South Africa. Social support was captured as the self-reported frequency with which close social contacts offered emotional, physical, financial, and informational support to respondents, standardized across the sample to increase interpretability. We used Cox proportional hazard models to evaluate how each support type affected mortality controlling for potential confounders, and assessed potential effect-modification by age and sex. Each of the four support domains had small positive associations with mortality, ranging from a hazard ratio per standard deviation of support of 1.04 [95% CI: 0.95,1.13] for financial support to 1.09 [95% CI: 0.99,1.18] for informational support. Associations were often stronger for females and younger individuals. We find baseline social support to be positively associated with mortality in rural South Africa. Possible explanations include that insufficient social support is not a strong driver of mortality risk in this setting, or that social support does not reach some necessary threshold to buffer against mortality. Additionally, it is possible that the social support measure did not capture more relevant aspects of support, or that our social support measures captured prior morbidity that attracted support before the study began. We highlight approaches to evaluate some of these hypotheses in future research.
在高收入国家，将各类社会支持与死亡率联系起来的机制已得到充分研究。然而，对于这些关系在不同社会经济背景下如何发挥作用，我们却知之甚少。我们研究了社会支持的四个领域——情感、物质、经济和信息支持——对生活在农村且资源有限环境中的老年人样本死亡率的影响。利用来自HAALSI的基线调查和纵向死亡率数据，我们评估了社会支持如何影响南非姆普马兰加农村地区5059名40岁及以上人群队列的死亡率。社会支持通过自我报告亲密社会联系人向受访者提供情感、物质、经济和信息支持的频率来衡量，并在样本中进行标准化以提高可解释性。我们使用Cox比例风险模型来评估每种支持类型如何在控制潜在混杂因素的情况下影响死亡率，并评估年龄和性别的潜在效应修正。四个支持领域中的每一个都与死亡率有小的正相关，经济支持每标准差的风险比为1.04 [95%置信区间：0.95,1.13]，信息支持为1.09 [95%置信区间：0.99,1.18]。女性和年轻人的关联通常更强。我们发现基线社会支持与南非农村地区的死亡率呈正相关。可能的解释包括，在这种情况下，社会支持不足并非死亡率风险的强大驱动因素，或者社会支持未达到抵御死亡的必要阈值。此外，社会支持措施可能未涵盖支持的更多相关方面，或者我们的社会支持措施捕捉到了研究开始前就已吸引支持的先前发病情况。我们强调了在未来研究中评估其中一些假设的方法。
