Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
Lancaster Medical School, Lancaster University, Lancaster, UK.
BMJ Open. 2022 May 24;12(5):e054584. doi: 10.1136/bmjopen-2021-054584.
To examine the socioeconomic and demographic drivers associated with polypharmacy (5-9 medicines), extreme polypharmacy (9-20 medicines) and increased medication count.
DESIGN, SETTING AND PARTICIPANTS: A total of 5509 participants, from two waves of the English North West Coast, Household Health Survey were analysed OUTCOME MEASURES: Logistic regression modelling was used to find associations with polypharmacy and extreme polypharmacy. A negative binomial regression identified associations with increased medication count. Descriptive statistics explored associations with medication management.
Age and number of health conditions account for the greatest odds of polypharmacy. ORs (95% CI) were greatest for those aged 65+ (3.87, 2.45 to 6.13) and for those with ≥5 health conditions (10.87, 5.94 to 19.88). Smaller odds were seen, for example, in those prescribed cardiovascular medications (3.08, 2.36 to 4.03), or reporting >3 emergency attendances (1.97, 1.23 to 3.17). Extreme polypharmacy was associated with living in a deprived neighbourhood (1.54, 1.06 to 2.26). The greatest risk of increased medication count was associated with age, number of health conditions and use of primary care services. Relative risks (95% CI) were greatest for those aged 65+ (2.51, 2.23 to 2.82), those with ≥5 conditions (10.26, 8.86 to 11.88) or those reporting >18 primary care visits (2.53, 2.18 to 2.93). Smaller risks were seen in, for example, respondents with higher levels of income deprivation (1.35, 1.03 to 1.77). Polypharmic respondents were more likely to report medication management difficulties associated with taking more than one medicine at a time (p<0.001). Furthermore, individuals reporting a mental health condition, were significantly more likely to consistently report difficulties managing their medication (p<0.001).
Age and number of health conditions are most associated with polypharmacy. Thus, delaying or preventing the onset of long-term conditions may help to reduce polypharmacy. Interventions to reduce income inequalities and health inequalities generally could support a reduction in polypharmacy, however, more research is needed in this area. Furthermore, increased prevention and support, particularly with medication management, for those with mental health conditions may reduce adverse medication effects.
研究与多药治疗（5-9 种药物）、极端多药治疗（9-20 种药物）和增加药物数量相关的社会经济和人口统计学驱动因素。
设计、地点和参与者：对来自英格兰西北海岸两次家庭健康调查的 5509 名参与者进行了分析。
使用逻辑回归模型寻找与多药治疗和极端多药治疗相关的关联。负二项回归确定了与增加药物数量相关的关联。描述性统计数据探讨了与药物管理相关的关联。
年龄和健康状况数量是多药治疗的最大相关因素。年龄在 65 岁以上（优势比 [OR]，95%置信区间 [CI]：3.87，2.45 至 6.13）和患有≥5 种健康状况（OR：10.87，5.94 至 19.88）的患者的 OR 值最大。例如，服用心血管药物（OR：3.08，2.36 至 4.03）或报告>3 次急诊就诊（OR：1.97，1.23 至 3.17）的患者，OR 值较小。极端多药治疗与生活在贫困社区（OR：1.54，1.06 至 2.26）相关。增加药物数量的最大风险与年龄、健康状况数量和使用初级保健服务相关。年龄在 65 岁以上（相对风险 [RR]，95%CI：2.51，2.23 至 2.82）、患有≥5 种疾病（RR：10.26，8.86 至 11.88）或报告>18 次初级保健就诊（RR：2.53，2.18 至 2.93）的患者 RR 值最大。例如，收入剥夺程度较高（RR：1.35，1.03 至 1.77）的患者风险较小。同时服用多种药物的患者更有可能报告与同时服用多种药物相关的药物管理困难（p<0.001）。此外，报告有心理健康状况的个体更有可能持续报告药物管理困难（p<0.001）。
年龄和健康状况数量与多药治疗最相关。因此，延迟或预防长期疾病的发生可能有助于减少多药治疗。减少收入不平等和健康不平等的干预措施可能有助于减少多药治疗，但在这方面需要更多的研究。此外，增加对心理健康状况患者的预防和支持，特别是在药物管理方面，可能会减少药物的不良反应。
